Sushrita Rakshit

Incoming PhD @ JHU | NLP-HAI-CSS

[Sushrita Rakshit @Sushrita22]

Hi everyone! I'm applying to PhD programs this cycle! I study how AI systems can understand and support human decision-making, at the intersection of NLP, Human-AI interaction, and behavioral science.

If you know any opportunities or are open to chat, please message me!

[Sushrita Rakshit @Sushrita22]

🧵 3/3
More here: https://t.co/FGGMBDOzBw

Currently, I'm spending time in industry (R&D with AI & NLP at Verizon) to see research applied in the real world and do some NLP pioneering for users. I'm excited to return to research and tackle socially impactful questions. Cheers!

[Sushrita Rakshit @Sushrita22]

Hi everyone! I'm applying to PhD programs this cycle! I study how AI systems can understand and support human decision-making, at the intersection of NLP, Human-AI interaction, and behavioral science.

If you know any opportunities or are open to chat, please message me!

[Sushrita Rakshit @Sushrita22]

🧵 2/3
Some highlights from my work:

#ACII: Emotion-aware agents
#NAACL: Email response predictors & multilingual dispute corpus
#ACL (in review): LLM simulations
#NeurIPS2025: Human-AI alignment review

My goal: improving AI to better understand + support human decision-making

[Sushrita Rakshit @Sushrita22]

@ethanachi Hi Ethan! I'd love to chat about this! Think I've worked on some things you'd be interested in.

[ACLRollingReview @ReviewAcl]

🚨 ARR is moving to 10-week cycles! 🚨
Starting after the Feb 2025 cycle, ARR will switch from 8-week to 10-week cycles.
📅 Check out the updated review schedule here: https://t.co/8NFBhFTIa9

[Sushrita Rakshit @Sushrita22]

Thrilled to receive my first two acceptances to the NAACL 2025 main conference! 🎉 So proud of all the hard work my collaborators and I put in to make this happen. Here's to the next steps! #NAACL2025 #Research

[ACL 2027 @aclmeeting]

❗️REMINDER 📢#ACL2025 is inviting nominations and self-nominations to the ACL 2025 programme committee (reviewers or area chair)ℹ️ https://t.co/YWQikbHxy3 deadline for nominations 🗓️ *now* 20 Dec 2024. 🙏

[Xin Eric Wang @xwang_lk]

Happy New Year! On the last day of 2024, I want to take a moment to reflect on what’s ahead in 2025. I don’t want to talk about buzzwords like "agents", instead, I’d like to summarize my thoughts with three keywords: Interactivity, Efficiency, and Humans.

- Interactivity: O1&3 are far from AGI as they only represent inner thoughts and can solve a very limited subset of real-world tasks. In contrast, most tasks require actual interaction with environments and real-time feedback. How can we make LLMs natively interactive? By natively, I mean without embedding them in an “agentic” framework. While the framework might be agentic, the LLMs themselves are not. To address this, LLMs need inner memory, inner feedback, and mechanisms to process new observations and make sequential decisions. I rarely see work addressing this. Achieving this will go beyond pre-training (which will end as we know) and instead require learning through direct interactions.

- Efficiency: While not a new direction, 2025 will be a turning point. Scaling has hit a wall—blindly increasing model and data sizes has reached the last part of the saturation curve. In addition to academia, major industry players in LLMs will (if they haven’t already) heavily invest in learning knowledge and skills more efficiently during both training and inference. This involves using higher-quality data and achieving greater results with fixed parameter sizes rather than relying on brute-force scaling. It is no longer the case we have to work on efficiency due to limited resources; it is the case we need to work on it to break the scaling bottleneck and bring LLMs to another level.

- Humans: This is not just about alignment. Aligning models with humans seems intuitive and appealing, but is it the right approach? Models and humans work very differently, even if training methods are inspired by so-called “cognitive” or “biological” ideas. On a behavioral level, aligning models to serve human purposes makes sense. However, in terms of their working mechanisms, models have their own processes, and counterintuitive ideas may be the most effective. For example, instead of feeding RGB images into self-driving cars, Tesla uses raw information, bypassing camera processing to preserve more data. Most importantly, in 2025, we need to focus on studying the differences—not just the similarities—between humans and models. This will help models better complement human abilities, or vice versa (which is more practical for new AI users). Remember, even if AGI is achieved, it will most likely differ fundamentally from human intelligence.

That’s it. I wanted to write this down after a wonderful New Year’s Eve dinner and a few drinks. I may elaborate more on these thoughts when I get the chance.