3 citations · 5 across the 6 of their papers we have counts for
6 papers
Context-Grounding Gains Are Mediated by Pre-existing Machinery: Auditing GRPO, SFT, and DPO
Prakhar Gupta, Vaibhav Gupta
Language models can ignore prompt evidence when it conflicts with memorized knowledge. Post-training can make models follow such evidence more reliably, but it is unclear whether t…
Bootstrapped Mixed Rewards for RL Post-Training: Injecting Canonical Action Order
Prakhar Gupta, Vaibhav Gupta
Post-training with reinforcement learning (RL) typically optimizes a single scalar objective and ignores structure in how solutions are produced. We ask whether a scalar hint towar…
Better World Models Can Lead to Better Post-Training Performance
Prakhar Gupta, Henry Conklin, Sarah-Jane Leslie +1
We study how explicit world-modeling objectives affect the internal representations and downstream capability of Transformers, using Rubik's Cubes as our training domain. We ask: (…
Revisiting In-Context Learning with Long Context Language Models
Jinheon Baek, Sun Jae Lee, Prakhar Gupta +3
In-Context Learning (ICL) is a technique by which language models make predictions based on examples provided in their input context. Previously, their context window size imposed…
Towards Implicit Bias Detection and Mitigation in Multi-Agent LLM Interactions
Angana Borah, Rada Mihalcea
As Large Language Models (LLMs) continue to evolve, they are increasingly being employed in numerous studies to simulate societies and execute diverse social tasks. However, LLMs a…
Leveraging Machine-Generated Rationales to Facilitate Social Meaning Detection in Conversations
Ritam Dutt, Zhen Wu, Kelly Shi +3
We present a generalizable classification approach that leverages Large Language Models (LLMs) to facilitate the detection of implicitly encoded social meaning in conversations. We…