436 citations · 1.4k across the 41 of their papers we have counts for
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To Think or Not to Think: The Hidden Cost of Meta-Training with Excessive CoT Examples
Vignesh Kothapalli, Ata Fatahibaarzi, Hamed Firooz +1
Chain-of-thought (CoT) prompting combined with few-shot in-context learning (ICL) has unlocked significant reasoning capabilities in large language models (LLMs). However, ICL with…
CoT-ICL Lab: A Synthetic Framework for Studying Chain-of-Thought Learning from In-Context Demonstrations
Vignesh Kothapalli, Hamed Firooz, Maziar Sanjabi
We introduce CoT-ICL Lab, a framework and methodology to generate synthetic tokenized datasets and systematically study chain-of-thought (CoT) in-context learning (ICL) in language…
Resprompt: Residual Connection Prompting Advances Multi-Step Reasoning in Large Language Models
Song Jiang, Zahra Shakeri, Aaron Chan +8
Chain-of-thought (CoT) prompting, which offers step-by-step problem-solving rationales, has impressively unlocked the reasoning potential of large language models (LLMs). Yet, the…
COFFEE: Counterfactual Fairness for Personalized Text Generation in Explainable Recommendation
Nan Wang, Qifan Wang, Yi-Chia Wang +5
As language models become increasingly integrated into our digital lives, Personalized Text Generation (PTG) has emerged as a pivotal component with a wide range of applications. H…
FRAME: Evaluating Rationale-Label Consistency Metrics for Free-Text Rationales
Aaron Chan, Shaoliang Nie, Liang Tan +4
Following how humans communicate, free-text rationales aim to use natural language to explain neural language model (LM) behavior. However, free-text rationales' unconstrained natu…
ER-Test: Evaluating Explanation Regularization Methods for Language Models
Brihi Joshi, Aaron Chan, Ziyi Liu +4
By explaining how humans would solve a given task, human rationales can provide strong learning signal for neural language models (LMs). Explanation regularization (ER) aims to imp…
Detection, Disambiguation, Re-ranking: Autoregressive Entity Linking as a Multi-Task Problem
Khalil Mrini, Shaoliang Nie, Jiatao Gu +3
We propose an autoregressive entity linking model, that is trained with two auxiliary tasks, and learns to re-rank generated samples at inference time. Our proposed novelties addre…
UNIREX: A Unified Learning Framework for Language Model Rationale Extraction
Aaron Chan, Maziar Sanjabi, Lambert Mathias +5
An extractive rationale explains a language model's (LM's) prediction on a given task instance by highlighting the text inputs that most influenced the prediction. Ideally, rationa…