7 citations · 17 across the 17 of their papers we have counts for
9 papers · 1 filter
Co-Evolving Actor-Conditioned Critics for Non-Verifiable Generation
Jinyoung Kim, Muhammad Khalifa, Lajanugen Logeswaran +4
Natural-language critiques provide supervision beyond scalar rewards for non-verifiable generation, which lacks deterministic verifiers. In critique-guided refinement, a critic giv…
Revisiting LLM Value Probing Strategies: Are They Robust and Expressive?
Siqi Shen, Mehar Singh, Lajanugen Logeswaran +3
There has been extensive research on assessing the value orientation of Large Language Models (LLMs) as it can shape user experiences across demographic groups. However, several ch…
If You Can't Use Them, Recycle Them: Optimizing Merging at Scale Mitigates Performance Tradeoffs
Muhammad Khalifa, Yi-Chern Tan, Arash Ahmadian +6
Model merging has shown great promise at combining expert models, but the benefit of merging is unclear when merging "generalist" models trained on many tasks. We explore merging i…
Understanding the Capabilities and Limitations of Large Language Models for Cultural Commonsense
Siqi Shen, Lajanugen Logeswaran, Moontae Lee +3
Large language models (LLMs) have demonstrated substantial commonsense understanding through numerous benchmark evaluations. However, their understanding of cultural commonsense re…
Small Language Models Need Strong Verifiers to Self-Correct Reasoning
Yunxiang Zhang, Muhammad Khalifa, Lajanugen Logeswaran +4
Self-correction has emerged as a promising solution to boost the reasoning performance of large language models (LLMs), where LLMs refine their solutions using self-generated criti…
Source-Aware Training Enables Knowledge Attribution in Language Models
Muhammad Khalifa, David Wadden, Emma Strubell +4
Large language models (LLMs) learn a vast amount of knowledge during pretraining, but they are often oblivious to the source(s) of such knowledge. We investigate the problem of int…
TOD-Flow: Modeling the Structure of Task-Oriented Dialogues
Sungryull Sohn, Yiwei Lyu, Anthony Liu +4
Task-Oriented Dialogue (TOD) systems have become crucial components in interactive artificial intelligence applications. While recent advances have capitalized on pre-trained langu…
From Heuristic to Analytic: Cognitively Motivated Strategies for Coherent Physical Commonsense Reasoning
Zheyuan Zhang, Shane Storks, Fengyuan Hu +4
Pre-trained language models (PLMs) have shown impressive performance in various language tasks. However, they are prone to spurious correlations, and often generate illusory inform…
Merging Generated and Retrieved Knowledge for Open-Domain QA
Yunxiang Zhang, Muhammad Khalifa, Lajanugen Logeswaran +3
Open-domain question answering (QA) systems are often built with retrieval modules. However, retrieving passages from a given source is known to suffer from insufficient knowledge…