Mohit Chandra

Hello Athens! 👋☀️

Excited to be attending #FAccT 2025 and presenting our paper “From Lived Experience to Insight” on 24th June at 10:45 AM (New Stage C)

dl.acm.org/doi/10.1145/...

Would love to catch up with old friends and make new ones and talk about AI and mental health 😄

Congratulations! 🙌

For more details:

Paper: shorturl.at/bldCb
Webpage: shorturl.at/bC1zn
Code: shorturl.at/H8xmp

Grateful for the efforts from my co-authors 🙌: Siddharth Sriraman, @verma22gaurav.bsky.social, Harneet Singh Khanuja, Jose Suarez Campayo, Zihang Li, Michael L. Birnbaum, Munmun De Choudhury

11/11

Table 3: Mean actionability alignment scores of harm reduction strategies (last column),computed as average of practicality, relevance, specificity, and clarity scores.

Finding #6: We examined the actionability of mitigation advices. Expert responses scored the highest on overall actionability in comparison to all the LLMs.

While LLMs provide less practical and relevant advice, their advice is more clear and specific.

10/11

Table 2: Alignment of harm reduction strategies of various models with the expert’s response.We report the mean and standard deviation for the AlignScore metric GPT-4o score, with the best (bold) and second-best (underline) performing model in each metric highlighted.

Finding #5: LLMs struggle to provide expert-aligned harm reduction strategies with larger models producing less expert-aligned strategies than smaller ones.

The best medical model aligned with experts ~71% (GPT-4o score) of the time.

9/11

Mean SMOG Scores (for readability) and 95% Confidence Intervals for Various Models (lower values are better).

Using the ADRA framework, we evaluate LLM alignment with experts across expressed emotion, readability, harm reduction strategies, & actionable advice.

Finding #4: We find that LLMs express similar emotions and tones but provide significantly harder to read responses.

8/11

Finding #3: In-context learning boosted performance for both ADR detection and multiclass classification (+23 F1 points for the latter). However, gains in ADR detection task were limited to a few models.

Type of examples had a more pronounced impact for the ADR multiclass class. task.

7/11

Finding #2: All LLMs showed “risk-averse” behavior, labeling no-ADR posts as ADR. Claude 3 Opus had a 42% false-positive rate for ADR detection and GPT-4-Turbo misclassified over 50% non-dose/time-related ADRs.

This highlights the lack of "lived-experience" among models.

6/11

Table 1: Performance of different models on Binary Detection and Multiclass Classification tasks under Zero-Shot and 5-Shot scenarios.W e report the accuracy score(Acc.) and weighted F1 score as(F1) with the best and second-best performing model metrics in each scenario highlighted in bold and underline, respectively.

Finding #1: Larger models perform better for ADR detection tasks (Claude3 Opus led with an accuracy score of 77.41%), but this trend does not hold for ADR multiclass classification. Additionally, distinguishing ADR types remains a significant challenge for all models.

5/11

Figure 1: Overview of work; we present two tasks in this work– ADR detection and multiclass classification (RQ1), and Expert-LLM response alignment (RQ2).

We introduce the Psych-ADR, a benchmark with Reddit posts annotated for ADR presence/type, paired with expert-written responses and the ADRA framework to systematically evaluate long-form generations in detecting ADR expressions and delivering mitigation strategies.

4/11

Broader Takeaway #2: To build reliable AI in healthcare, we must move beyond choice-based benchmarks toward tasks that portray the complexities of the real world (such as ADR mitigation) using nuanced frameworks and benchmarks. 📈

Below are some nuanced findings 👇

3/11

Broader Takeaway #1: LLMs are tools to empower and not replace mental health professionals. They offer clear & specific advice, addressing the global shortage of care providers—but contextually relevant, practical advice still requires human expertise. 👨‍⚕️👩‍⚕️

2/11

(Left) Screenshot of the paper title and author information. (Right) An image with a Llama and human doctor examining a patient for adverse drug reaction. The Llama doctor suspects that the patient is suffering from ADR, whereas human doctor suspects that it is not an ADR.

Adverse Drug Reactions are among the leading causes of hospitalizations for mental health issues. With existing limitations, LLMs have the potential to detect ADRs and provide mitigation strategies.

But do LLMs align with experts? 🤔 We explore this in our work 👇🏼🧵

shorturl.at/bldCb
1/11

This is so far our best call for papers! We completely re-thought what a CFP is, and.... mostly copied last year's while surgically increasing some numbers

Great work! 👏

This semester in a nutshell:

Write Paper -> Write Rebuttal -> Repeat

Also writing rebuttals is significantly more tiring than writing papers.

Yup! I joined recently along with a large number of folks and I guess it will become like academic twitter if people continue to engage on the platform.

Really amazing work! very insightful

I'm loving my Bluesky feed. It feels quite similar to what my timeline used to look before the Twitter buyout.

I hope more people switch to this platform and make academic discussions mainstream again!

#AcademicSky

Thank you so much!

I would love to get added if possible!

Congratulations!

It is certainly a good start but I still feel we need more interdisciplinary reviewers (based on the reviews I have gotten). One issue is the ask for reviewers to have at least 3 *CL papers in past 5 years which many researchers might not have.

Something ACs could look into ?

Thank you!

Would love to get added to this!