200 citations · 1.3k across the 72 of their papers we have counts for
76 papers
Explore Theory of Mind: Program-guided adversarial data generation for theory of mind reasoning
Melanie Sclar, Jane Yu, Maryam Fazel-Zarandi +4
Do large language models (LLMs) have theory of mind? A plethora of papers and benchmarks have been introduced to evaluate if current models have been able to develop this key abili…
Efficient Tool Use with Chain-of-Abstraction Reasoning
Silin Gao, Jane Dwivedi-Yu, Ping Yu +7
To achieve faithful reasoning that aligns with human expectations, large language models (LLMs) need to ground their reasoning to real-world knowledge (e.g., web facts, math and ph…
PathFinder: Guided Search over Multi-Step Reasoning Paths
Olga Golovneva, Sean O'Brien, Ramakanth Pasunuru +4
With recent advancements in large language models, methods like chain-of-thought prompting to elicit reasoning chains have been shown to improve results on reasoning tasks. However…
The ART of LLM Refinement: Ask, Refine, and Trust
Kumar Shridhar, Koustuv Sinha, Andrew Cohen +6
In recent years, Large Language Models (LLMs) have demonstrated remarkable generative abilities, but can they judge the quality of their own generations? A popular concept, referre…
BLESS: Benchmarking Large Language Models on Sentence Simplification
Tannon Kew, Alison Chi, Laura Vásquez-Rodríguez +4
We present BLESS, a comprehensive performance benchmark of the most recent state-of-the-art large language models (LLMs) on the task of text simplification (TS). We examine how wel…
Crystal: Introspective Reasoners Reinforced with Self-Feedback
Jiacheng Liu, Ramakanth Pasunuru, Hannaneh Hajishirzi +2
Extensive work has shown that the performance and interpretability of commonsense reasoning can be improved via knowledge-augmented reasoning methods, where the knowledge that unde…
Sub-network Discovery and Soft-masking for Continual Learning of Mixed Tasks
Zixuan Ke, Bing Liu, Wenhan Xiong +2
Continual learning (CL) has two main objectives: preventing catastrophic forgetting (CF) and encouraging knowledge transfer (KT). The existing literature mainly focused on overcomi…
Walking Down the Memory Maze: Beyond Context Limit through Interactive Reading
Howard Chen, Ramakanth Pasunuru, Jason Weston +1
Large language models (LLMs) have advanced in large strides due to the effectiveness of the self-attention mechanism that processes and compares all tokens at once. However, this m…
DOMINO: A Dual-System for Multi-step Visual Language Reasoning
Peifang Wang, Olga Golovneva, Armen Aghajanyan +4
Visual language reasoning requires a system to extract text or numbers from information-dense images like charts or plots and perform logical or arithmetic reasoning to arrive at a…
Branch-Solve-Merge Improves Large Language Model Evaluation and Generation
Swarnadeep Saha, Omer Levy, Asli Celikyilmaz +3
Large Language Models (LLMs) are frequently used for multi-faceted language generation and evaluation tasks that involve satisfying intricate user constraints or taking into accoun…
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…
Chain-of-Verification Reduces Hallucination in Large Language Models
Shehzaad Dhuliawala, Mojtaba Komeili, Jing Xu +4
Generation of plausible yet incorrect factual information, termed hallucination, is an unsolved issue in large language models. We study the ability of language models to deliberat…
Scaling Autoregressive Multi-Modal Models: Pretraining and Instruction Tuning
Lili Yu, Bowen Shi, Ramakanth Pasunuru +24
We present CM3Leon (pronounced "Chameleon"), a retrieval-augmented, token-based, decoder-only multi-modal language model capable of generating and infilling both text and images. C…
Don't throw away your value model! Generating more preferable text with Value-Guided Monte-Carlo Tree Search decoding
Jiacheng Liu, Andrew Cohen, Ramakanth Pasunuru +3
Inference-time search algorithms such as Monte-Carlo Tree Search (MCTS) may seem unnecessary when generating natural language text based on state-of-the-art reinforcement learning…
Shepherd: A Critic for Language Model Generation
Tianlu Wang, Ping Yu, Xiaoqing Ellen Tan +7
As large language models improve, there is increasing interest in techniques that leverage these models' capabilities to refine their own outputs. In this work, we introduce Shephe…
RLCD: Reinforcement Learning from Contrastive Distillation for Language Model Alignment
Kevin Yang, Dan Klein, Asli Celikyilmaz +2
We propose Reinforcement Learning from Contrastive Distillation (RLCD), a method for aligning language models to follow principles expressed in natural language (e.g., to be more h…
Understanding In-Context Learning via Supportive Pretraining Data
Xiaochuang Han, Daniel Simig, Todor Mihaylov +3
In-context learning (ICL) improves language models' performance on a variety of NLP tasks by simply demonstrating a handful of examples at inference time. It is not well understood…
Open-Domain Text Evaluation via Contrastive Distribution Methods
Sidi Lu, Hongyi Liu, Asli Celikyilmaz +2
Recent advancements in open-domain text generation, driven by the power of large pre-trained language models (LLMs), have demonstrated remarkable performance. However, assessing th…
Large Language Model Programs
Imanol Schlag, Sainbayar Sukhbaatar, Asli Celikyilmaz +4
In recent years, large pre-trained language models (LLMs) have demonstrated the ability to follow instructions and perform novel tasks from a few examples. The possibility to param…
Gender Biases in Automatic Evaluation Metrics for Image Captioning
Haoyi Qiu, Zi-Yi Dou, Tianlu Wang +2
Model-based evaluation metrics (e.g., CLIPScore and GPTScore) have demonstrated decent correlations with human judgments in various language generation tasks. However, their impact…
Look-back Decoding for Open-Ended Text Generation
Nan Xu, Chunting Zhou, Asli Celikyilmaz +1
Given a prefix (context), open-ended generation aims to decode texts that are coherent, which do not abruptly drift from previous topics, and informative, which do not suffer from…
OPT-R: Exploring the Role of Explanations in Finetuning and Prompting for Reasoning Skills of Large Language Models
Badr AlKhamissi, Siddharth Verma, Ping Yu +3
In this paper, we conduct a thorough investigation into the reasoning capabilities of Large Language Models (LLMs), focusing specifically on the Open Pretrained Transformers (OPT)…
RECKONING: Reasoning through Dynamic Knowledge Encoding
Zeming Chen, Gail Weiss, Eric Mitchell +2
Recent studies on transformer-based language models show that they can answer questions by reasoning over knowledge provided as part of the context (i.e., in-context reasoning). Ho…
Augmented Language Models: a Survey
Grégoire Mialon, Roberto Dessì, Maria Lomeli +10
This survey reviews works in which language models (LMs) are augmented with reasoning skills and the ability to use tools. The former is defined as decomposing a potentially comple…