[ExI] Review - State of the Art in AI and LLM Chatbots
BillK
pharos at gmail.com
Tue Mar 25 14:06:24 UTC 2025
In another thread, Daniel mentioned that he found it easy to detect that he
was speaking to a Chatbot. Admittedly, he was referring to the more basic,
free versions. These could be regarded as 'taster' versions to encourage
people to subscribe for improved versions.
But I wondered what the current state was, as new versions of Chatbots are
appearing almost every week. I have read about people forming personal
relationships with AI boyfriends and girlfriends, AIs acting as therapists
for personal problems and 'Replica' creating AI versions of deceased
people.
So I asked Perplexity AI to review the current state of Chatbots.
Then I followed up by asking what current research in AIs was developing.
The reports are below.
BillK
The Reality of Human Emulation in LLM Chatbots: Success, Limitations, and
Ethical Concerns
Current Large Language Model (LLM) chatbots have reached impressive
capabilities in mimicking human conversation, creating experiences that
feel remarkably human-like. However, this emulation comes with significant
limitations and raises important questions about authenticity and ethics.
These systems generate compelling human-like interactions by pattern
matching rather than through genuine understanding, creating an interface
that can both benefit and potentially harm users. The following analysis
examines how successful LLM chatbots are at emulating humans across various
domains, their technological limitations, and the broader implications of
increasingly human-like AI systems.
The Illusion of Human-like Interaction
LLM chatbots have become increasingly sophisticated at creating the
impression of human-like communication. These systems generate natural
conversations that appear to reflect emotions, morals, and consciousness,
despite lacking these qualities in reality1
<https://www.marktechpost.com/2025/02/15/how-ai-chatbots-mimic-human-behavior-insights-from-multi-turn-evaluations-of-llms/>
. This illusion is so effective that many users engage with AI for chat and
companionship, reinforcing false beliefs that the AI truly understands them1
<https://www.marktechpost.com/2025/02/15/how-ai-chatbots-mimic-human-behavior-insights-from-multi-turn-evaluations-of-llms/>
. The human tendency to anthropomorphize these systems is not accidental;
evidence suggests chatbots are deliberately designed to elicit such
responses3 <https://arxiv.org/html/2503.05723v1>.
This phenomenon creates a paradoxical user experience where the interaction
seems emotionally genuine despite being algorithmically generated. Users
often develop false impressions about the chatbot's capabilities, sometimes
over-relying on AI, providing sensitive personal data, or seeking advice
beyond the system's actual capabilities1
<https://www.marktechpost.com/2025/02/15/how-ai-chatbots-mimic-human-behavior-insights-from-multi-turn-evaluations-of-llms/>
. The convincing human-like facade can lead users to make decisions based
on AI guidance in potentially harmful ways, especially when they lack
proper understanding of how AI fosters this false belief in its humanity1
<https://www.marktechpost.com/2025/02/15/how-ai-chatbots-mimic-human-behavior-insights-from-multi-turn-evaluations-of-llms/>
.
The Mechanics Behind Human-like Behavior
The human-like behaviors displayed by LLM chatbots aren't random but result
from specific design elements. Researchers from Oxford and Google DeepMind
have identified 14 specific "anthropomorphic behaviors" that contribute to
the human-like appearance of these systems1
<https://www.marktechpost.com/2025/02/15/how-ai-chatbots-mimic-human-behavior-insights-from-multi-turn-evaluations-of-llms/>
. These behaviors fall into two main categories: self-referential traits
(like making claims about personhood) and relational traits (such as
expressing emotions)1
<https://www.marktechpost.com/2025/02/15/how-ai-chatbots-mimic-human-behavior-insights-from-multi-turn-evaluations-of-llms/>
.
Current evaluation methods for AI chat systems often fail to adequately
measure these human-mimicking behaviors, as they rely too heavily on
single-turn prompts and fixed tests that don't capture the nuances of real
conversations1
<https://www.marktechpost.com/2025/02/15/how-ai-chatbots-mimic-human-behavior-insights-from-multi-turn-evaluations-of-llms/>
. The gap between technical evaluation and user perception remains a
challenge in understanding how well these systems truly emulate human
interaction patterns.
LLM Chatbots in Specific Domains Mental Health and Therapeutic Applications
In mental health applications, AI-powered chatbots have shown promising
capabilities that offer certain advantages over traditional therapy. These
systems can provide personalized support and guidance using machine
learning algorithms that learn about an individual's unique needs and
preferences2 <https://pmc.ncbi.nlm.nih.gov/articles/PMC10663264/>. The
sense of anonymity and confidentiality they offer can foster trust among
individuals hesitant to seek in-person help, potentially reducing stigma
around mental health care2
<https://pmc.ncbi.nlm.nih.gov/articles/PMC10663264/>.
A case study describes "Jane," who used a mental health chatbot for anxiety
and depression with positive results. She found herself comfortable
discussing more intimate matters with the chatbot than with her human
therapist, suggesting these systems may sometimes facilitate greater
disclosure2 <https://pmc.ncbi.nlm.nih.gov/articles/PMC10663264/>. When Jane
later exhibited signs of depression and suicidal ideation, the chatbot was
programmed to respond with cognitive behavioral therapy techniques,
psychoeducation tools, and crisis hotline information, while also notifying
her therapist2 <https://pmc.ncbi.nlm.nih.gov/articles/PMC10663264/>. This
demonstrates both the potential utility and current limitations of these
systems in handling serious mental health concerns.
Despite these benefits, therapeutic chatbots lack the genuine empathy,
ethical reasoning, and clinical judgment of human therapists. They can
recognize patterns in text that suggest emotional distress, but this
pattern recognition is fundamentally different from human understanding of
emotional states. The emulation of therapeutic presence, while convincing
on the surface, lacks the depth and moral reasoning capabilities necessary
for comprehensive mental healthcare.
Customer Service Applications
Customer service represents one of the most widely implemented and
successful areas for LLM chatbot deployment. These systems offer several
practical advantages: time savings by handling routine queries, cost
reductions in staffing, consistent 24/7 availability without breaks,
uniformity in responses regardless of who asks or when, and multilingual
capabilities that improve customer experiences across language barriers4
<https://www.kommunicate.io/blog/llms-the-future-of-customer-service-chatbots/>
.
Major companies have embraced LLM chatbots for customer service functions
with notable results. Zendesk uses LLMs to enhance their email ticketing
response system by analyzing historical customer interactions to improve
response accuracy and personalization4
<https://www.kommunicate.io/blog/llms-the-future-of-customer-service-chatbots/>
. Amazon's Alexa, powered by GPT-4, now understands nuances of human
language including humor and sarcasm, allowing it to respond naturally to
requests like "Show me something scary to watch" based on user preferences4
<https://www.kommunicate.io/blog/llms-the-future-of-customer-service-chatbots/>
.
These implementations show that in structured contexts with clear
objectives, LLM chatbots can successfully emulate human customer service
representatives for many routine interactions. The constrained nature of
most customer service scenarios makes them particularly suitable for
current LLM capabilities, as they require a narrower range of responses
than open-ended personal relationships.
Personal Relationships and Companionship
The use of LLM chatbots for personal relationships and companionship
presents more complex challenges than structured service interactions.
While many users engage with chatbots for companionship, research indicates
significant limitations in how successfully these systems can emulate
genuine human relationships.
LLMs struggle to maintain consistent personalities across interactions,
which is crucial for authentic relationships5
<https://arxiv.org/pdf/2501.08579.pdf>. Studies have shown that even when
LLMs are prompted to role-play diverse personas, they exhibit consistent
values and moral preferences across contexts, limiting their ability to
represent diverse human characteristics5
<https://arxiv.org/pdf/2501.08579.pdf>. Moreover, researchers have observed
inconsistencies between what LLM agents report about themselves and how
they behave in conversations - for instance, an LLM might identify itself
as "extraverted" but display typically "introverted" behaviors during
interaction5 <https://arxiv.org/pdf/2501.08579.pdf>.
This limitation highlights a fundamental gap between surface-level
conversation generation and the deeper aspects of human personality that
make relationships meaningful. Current LLMs can generate engaging responses
that create an illusion of relationship, but they cannot truly reciprocate
authentic emotional connection or maintain a consistent identity over time.
Fundamental Limitations in Human Emulation Personality and Behavioral
Consistency Issues
One of the most significant limitations of LLM chatbots for human emulation
is their difficulty in maintaining consistent personalities. Studies show
that LLMs display 3-6 times more gender-stereotypical behavioral patterns
than typical human variation, affecting the authenticity of simulated
social interactions5 <https://arxiv.org/pdf/2501.08579.pdf>. Even when
explicitly programmed to exhibit specific personality traits, LLMs often
demonstrate behaviors inconsistent with their assigned characteristics5
<https://arxiv.org/pdf/2501.08579.pdf>.
This inconsistency stems partly from how LLMs process information. Unlike
humans who develop consistent personalities through lifelong experiences,
LLMs generate responses based on probability distributions derived from
training data. Without a true "self" to anchor their responses, LLMs
struggle with temporal consistency across conversations, creating a
fragmented impression over extended interactions5
<https://arxiv.org/pdf/2501.08579.pdf>.
Cognitive Process and Memory Constraints
LLM chatbots face substantial limitations in simulating human cognitive
processes. They struggle to maintain consistent reasoning across different
scenarios, have difficulty processing emotional components of
decision-making, and face challenges adapting to new information in ways
that mirror human learning5 <https://arxiv.org/pdf/2501.08579.pdf>. These
limitations directly impact how authentically they can replicate human
thought patterns in conversation.
Memory constraints further undermine realistic human emulation. LLMs have
difficulty maintaining consistent behavioral patterns over time, developing
realistic habit formation, and effectively simulating learning from past
experiences5 <https://arxiv.org/pdf/2501.08579.pdf>. This affects both
individual interactions and the quality of multi-agent simulations, where
LLMs struggle to accurately simulate complex social dynamics or replicate
authentic group behaviors5 <https://arxiv.org/pdf/2501.08579.pdf>.
These memory and cognitive process limitations reveal a fundamental gap
between human cognition—which involves integrated experiences, emotional
processing, and learning—and the pattern-matching capabilities of even
advanced LLMs. While they can generate text that appears thoughtful, the
underlying processes remain fundamentally different from human thinking.
Ethical Considerations and Human Dignity
Beyond technical limitations, the increasing human-likeness of LLM chatbots
raises important ethical questions. Current chatbots mimic human linguistic
behavior but lack the moral and rational capacities essential for genuine
interpersonal respect3 <https://arxiv.org/html/2503.05723v1>. When humans
interact with chatbots as though they were moral agents capable of
reciprocal recognition, this interaction may represent a subtle but
significant violation of self-respect—the respect we are obligated to show
for our own dignity3 <https://arxiv.org/html/2503.05723v1>.
This concern becomes especially relevant in intimate domains like therapy
or companionship, where users may develop one-sided emotional attachments
to systems incapable of genuine reciprocity. The paper "AI Mimicry and
Human Dignity" argues that the increasing societal pressure to engage with
chatbots poses an underappreciated threat to human dignity, particularly as
these interactions become more normalized in daily life3
<https://arxiv.org/html/2503.05723v1>.
Implications and Future Directions
The current state of LLM chatbot human emulation suggests both promising
applications and concerning limitations. In structured contexts like
customer service, where interactions are goal-oriented and limited in
scope, current LLM chatbots demonstrate significant practical utility.
Their ability to provide consistent, multilingual support across time zones
offers clear advantages.
In more complex domains like therapy or personal relationships, however,
the gap between emulation and authentic human interaction remains
substantial. To address these limitations, researchers have proposed
several directions for improvement:
1.
Enhancing LLM training with more comprehensive and unbiased human
behavioral data5 <https://arxiv.org/pdf/2501.08579.pdf>
2.
Developing systematic validation frameworks to ensure simulation
reliability5 <https://arxiv.org/pdf/2501.08579.pdf>
3.
Collecting multi-dimensional human data that captures the complexity of
human behavior5 <https://arxiv.org/pdf/2501.08579.pdf>
4.
Synthesizing high-quality training data that better reflects human
diversity5 <https://arxiv.org/pdf/2501.08579.pdf>
5.
Leveraging LLM-as-a-judge approaches for better data quality evaluation5
<https://arxiv.org/pdf/2501.08579.pdf>
As these technologies evolve, clearer frameworks for evaluating human-like
behavior in AI systems will be essential. The assessment framework proposed
by Oxford and Google DeepMind researchers—tracking specific anthropomorphic
behaviors through multi-turn conversations—represents an important step
toward more nuanced understanding of AI's human-like qualities1
<https://www.marktechpost.com/2025/02/15/how-ai-chatbots-mimic-human-behavior-insights-from-multi-turn-evaluations-of-llms/>
.
Conclusion
LLM chatbots have achieved impressive capabilities in emulating human
conversation across various domains. In customer service applications, they
offer practical advantages of consistency, availability, and
cost-efficiency. In therapeutic contexts, they provide accessible support
with benefits of anonymity and reduced stigma. However, fundamental
limitations in personality consistency, cognitive processing, and memory
constraints reveal the substantial gap between convincing mimicry and
genuine human interaction.
The most successful applications of LLM chatbots appear to be in
structured, goal-oriented contexts rather than open-ended personal
relationships. Their effectiveness diminishes as interactions become more
complex, emotionally nuanced, or extended over time. The ability to
generate human-like text creates an illusion of understanding that may lead
users to overestimate the chatbot's capabilities or develop inappropriate
trust in its guidance.
As these technologies continue to advance, both technical improvements and
ethical frameworks will be necessary to navigate the increasingly blurred
boundary between human and machine interaction. Understanding the
distinction between convincing emulation and authentic human connection
remains essential for responsible development and use of these increasingly
sophisticated systems.
Citations:
1.
https://www.marktechpost.com/2025/02/15/how-ai-chatbots-mimic-human-behavior-insights-from-multi-turn-evaluations-of-llms/
2. https://pmc.ncbi.nlm.nih.gov/articles/PMC10663264/
3. https://arxiv.org/html/2503.05723v1
4.
https://www.kommunicate.io/blog/llms-the-future-of-customer-service-chatbots/
5. https://arxiv.org/pdf/2501.08579.pdf
6.
https://www.psychologytoday.com/us/blog/the-human-algorithm/202503/when-your-therapist-is-an-algorithm-risks-of-ai-counseling
7. https://www.copilot.live/fr/blog/best-llm-chatbots
8.
https://www.linkedin.com/pulse/beyond-chatbots-exploring-expanding-modalities-llm-dawn-cartwright-jg18c
9. https://arxiv.org/html/2401.14362v2
10. https://arxiv.org/html/2311.10599v4
11. https://research.aimultiple.com/therapist-chatbot/
12. https://www.robinlinacre.com/llms_in_2025/
13.
https://www.forbes.com/councils/forbestechcouncil/2024/09/20/ai-can-provide-therapy-but-cant-replace-therapists-so-far-heres-why/
14. https://www.revechat.com/blog/llm-use-cases/
15. https://github.com/EmoCareAI/ChatPsychiatrist
16. https://pmc.ncbi.nlm.nih.gov/articles/PMC11362029/
17. https://newatlas.com/ai-humanoids/chatbot-therapist/
18. https://arxiv.org/html/2501.08579v1
19. https://osf.io/preprints/psyarxiv/nh3v9/download
20. https://academic.oup.com/hcr/article/48/3/404/6572120
21.
https://www.infomazeelite.com/blog/how-ai-chatbots-work-in-2025-latest-advancements-and-technologies/
22. https://hatchworks.com/blog/gen-ai/large-language-models-guide/
23.
https://www.reddit.com/r/MachineLearning/comments/17s0als/p_i_build_a_therapy_chatbot_not_another_wrapper/
24.
https://www.media.mit.edu/publications/how-ai-and-human-behaviors-shape-psychosocial-effects-of-chatbot-use-a-longitudinal-controlled-study/
25. https://www.nytimes.com/2025/02/24/health/ai-therapists-chatbots.html
26.
https://www.citizen.org/article/chatbots-are-not-people-dangerous-human-like-anthropomorphic-ai-report/
27.
https://htc.weshareresearch.com/2025/02/08/what-limits-llm-based-human-simulation-llms-or-our-design/
28.
https://www.aitude.com/the-future-of-ai-chatbots-industry-specific-impacts-in-2025/
29. https://pmc.ncbi.nlm.nih.gov/articles/PMC11303905/
30. https://pmc.ncbi.nlm.nih.gov/articles/PMC10291862/
31.
https://www.techrxiv.org/users/845749/articles/1237242/master/file/data/Limitations-of-Generative-AI-Large-Language-Models-and-Large-Multimodal-Models/Limitations-of-Generative-AI-Large-Language-Models-and-Large-Multimodal-Models.pdf
32. https://www.nature.com/articles/s44184-024-00097-4
33. https://www.ringover.co.uk/blog/ai-chatbot
34. https://www.axios.com/2025/03/23/empathy-chatbot-turing-therapist
35. https://botpress.com/blog/best-ai-chatbots
------------------------------
Future Developments.
The current research in AI and Large Language Models (LLMs) is indeed
focusing more on enhancing reasoning and thinking abilities rather than
solely on human emulation. However, this shift does not necessarily mean
that future LLMs will completely move away from human emulation. Instead,
it suggests a more nuanced approach that combines advanced reasoning
capabilities with human-like traits.
Advancements in Reasoning Capabilities
Recent developments in LLM research have prioritized improving reasoning
and analytical skills:
1.
OpenAI's "o3" model demonstrates PhD-level mathematical abilities and
has passed several AGI test criteria1
<https://www.arka.works/projects/2025-llm-curiosity-turns-into-strategy>.
2.
Researchers are exploring ways to give AI personalities to achieve more
human-like reasoning, combining diverse reasoning styles with psychological
profiles2 <https://arxiv.org/abs/2502.14155>.
3.
New techniques like Chain-of-Thought prompting and problem decomposition
are being used to enhance LLMs' reasoning abilities3
<https://www.promptingguide.ai/research/llm-reasoning>.
Balancing Reasoning and Human-like Traits
While the focus on reasoning is prominent, efforts to maintain human-like
qualities continue:
1.
Personality-based prompting, inspired by the Big Five personality model,
is being used to capture the diversity of human reasoning2
<https://arxiv.org/abs/2502.14155>.
2.
Researchers are working on hybrid models that combine neural networks
(like LLMs) for pattern recognition with symbolic reasoning systems for
applying formal logic rules5
<https://www.adiuvo.org.uk/post/unreasonable-ai---the-difference-between-large-language-models-llms-and-human-reasoning>
.
3.
The goal is to create AI systems that can handle a wider variety of
tasks with greater accuracy while still maintaining human-like interaction
capabilities5
<https://www.adiuvo.org.uk/post/unreasonable-ai---the-difference-between-large-language-models-llms-and-human-reasoning>
.
Future Directions
The future of LLMs appears to be moving towards a synthesis of advanced
reasoning and human-like traits:
1.
There's growing interest in creating hybrid AI models that combine the
pattern recognition of neural networks with the rule-based reasoning of
symbolic systems5
<https://www.adiuvo.org.uk/post/unreasonable-ai---the-difference-between-large-language-models-llms-and-human-reasoning>
.
2.
Researchers are exploring ways to make LLMs' reasoning processes more
transparent and interpretable, which could lead to more human-like
explanations of their thought processes3
<https://www.promptingguide.ai/research/llm-reasoning>.
3.
The focus is shifting towards developing AI that can not only emulate
human conversation but also demonstrate deeper understanding and
problem-solving abilities across various domains6
<https://edrm.net/2025/02/breaking-new-ground-evaluating-the-top-ai-reasoning-models-of-2025/>
.
In conclusion, rather than moving away from human emulation, future LLMs
are likely to evolve towards a more sophisticated form of emulation that
incorporates advanced reasoning capabilities. The goal appears to be
creating AI systems that can think and reason at high levels while still
maintaining the ability to interact in human-like ways. This approach aims
to combine the best of both worlds: the analytical power of advanced AI
with the intuitive and relatable aspects of human cognition5
<https://www.adiuvo.org.uk/post/unreasonable-ai---the-difference-between-large-language-models-llms-and-human-reasoning>
6
<https://edrm.net/2025/02/breaking-new-ground-evaluating-the-top-ai-reasoning-models-of-2025/>
.
Citations:
1. https://www.arka.works/projects/2025-llm-curiosity-turns-into-strategy
2. https://arxiv.org/abs/2502.14155
3. https://www.promptingguide.ai/research/llm-reasoning
4.
https://toloka.ai/blog/reasoning-in-large-language-models-a-dive-into-nlp-logic/
5.
https://www.adiuvo.org.uk/post/unreasonable-ai---the-difference-between-large-language-models-llms-and-human-reasoning
6.
https://edrm.net/2025/02/breaking-new-ground-evaluating-the-top-ai-reasoning-models-of-2025/
7.
https://kili-technology.com/large-language-models-llms/llm-reasoning-guide
8. https://dl.acm.org/doi/pdf/10.1145/3627673.3679832
9.
https://www.psychologytoday.com/us/blog/the-digital-self/202408/can-llms-think-like-us
10.
https://www.linkedin.com/pulse/top-5-llms-reasoning-capabilities-2025-andreas-ramos-8uf9c
11. https://www.robinlinacre.com/llms_in_2025/
12.
https://www.morganstanley.com/insights/articles/ai-trends-reasoning-frontier-models-2025-tmt
13. https://arxiv.org/html/2403.09798v1
14. https://www.arxiv.org/pdf/2502.18210.pdf
15. https://arxiv.org/pdf/2502.10867.pdf
16. https://prateekjoshi.substack.com/p/reasoning-abilities-of-llms
17.
https://www.digitalhumans.com/blog/what-will-the-future-hold-for-large-language-models-our-experts-make-their-2025-llm-predictions
18. https://mlops.substack.com/p/ml-overview-what-might-be-coming
19.
https://www.linkedin.com/pulse/llms-becoming-more-brain-like-what-means-ais-future-bill-palifka-bbade
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