Search Results for author: Vidhisha Balachandran

Found 23 papers, 17 papers with code

Investigating the Effect of Background Knowledge on Natural Questions

no code implementations NAACL (DeeLIO) 2021 Vidhisha Balachandran, Bhuwan Dhingra, Haitian Sun, Michael Collins, William Cohen

We create a subset of the NQ data, Factual Questions (FQ), where the questions have evidence in the KB in the form of paths that link question entities to answer entities but still must be answered using text, to facilitate further research into KB integration methods.

Natural Questions Retrieval

MEDIQ: Question-Asking LLMs for Adaptive and Reliable Clinical Reasoning

1 code implementation3 Jun 2024 Shuyue Stella Li, Vidhisha Balachandran, Shangbin Feng, Jonathan Ilgen, Emma Pierson, Pang Wei Koh, Yulia Tsvetkov

We develop a reliable Patient system and prototype several Expert systems, first showing that directly prompting state-of-the-art LLMs to ask questions degrades the quality of clinical reasoning, indicating that adapting LLMs to interactive information-seeking settings is nontrivial.

Question Answering

Learning Syntax Without Planting Trees: Understanding When and Why Transformers Generalize Hierarchically

1 code implementation25 Apr 2024 Kabir Ahuja, Vidhisha Balachandran, Madhur Panwar, Tianxing He, Noah A. Smith, Navin Goyal, Yulia Tsvetkov

Transformers trained on natural language data have been shown to learn its hierarchical structure and generalize to sentences with unseen syntactic structures without explicitly encoding any structural bias.

Inductive Bias Language Modelling

Don't Hallucinate, Abstain: Identifying LLM Knowledge Gaps via Multi-LLM Collaboration

1 code implementation1 Feb 2024 Shangbin Feng, Weijia Shi, Yike Wang, Wenxuan Ding, Vidhisha Balachandran, Yulia Tsvetkov

Despite efforts to expand the knowledge of large language models (LLMs), knowledge gaps -- missing or outdated information in LLMs -- might always persist given the evolving nature of knowledge.

Retrieval

Fine-grained Hallucination Detection and Editing for Language Models

no code implementations12 Jan 2024 Abhika Mishra, Akari Asai, Vidhisha Balachandran, Yizhong Wang, Graham Neubig, Yulia Tsvetkov, Hannaneh Hajishirzi

On our benchmark, our automatic and human evaluations show that FAVA significantly outperforms ChatGPT and GPT-4 on fine-grained hallucination detection, and edits suggested by FAVA improve the factuality of LM-generated text.

Hallucination Retrieval

P^3SUM: Preserving Author's Perspective in News Summarization with Diffusion Language Models

1 code implementation16 Nov 2023 YuHan Liu, Shangbin Feng, Xiaochuang Han, Vidhisha Balachandran, Chan Young Park, Sachin Kumar, Yulia Tsvetkov

In this work, we take a first step towards designing summarization systems that are faithful to the author's intent, not only the semantic content of the article.

News Summarization

KGQuiz: Evaluating the Generalization of Encoded Knowledge in Large Language Models

1 code implementation15 Oct 2023 Yuyang Bai, Shangbin Feng, Vidhisha Balachandran, Zhaoxuan Tan, Shiqi Lou, Tianxing He, Yulia Tsvetkov

To gain a better understanding of LLMs' knowledge abilities and their generalization, we evaluate 10 open-source and black-box LLMs on the KGQuiz benchmark across the five knowledge-intensive tasks and knowledge domains.

Multiple-choice World Knowledge

Resolving Knowledge Conflicts in Large Language Models

1 code implementation2 Oct 2023 Yike Wang, Shangbin Feng, Heng Wang, Weijia Shi, Vidhisha Balachandran, Tianxing He, Yulia Tsvetkov

To this end, we introduce KNOWLEDGE CONFLICT, an evaluation framework for simulating contextual knowledge conflicts and quantitatively evaluating to what extent LLMs achieve these goals.

Knowledge Crosswords: Geometric Knowledge Reasoning with Large Language Models

1 code implementation2 Oct 2023 Wenxuan Ding, Shangbin Feng, YuHan Liu, Zhaoxuan Tan, Vidhisha Balachandran, Tianxing He, Yulia Tsvetkov

The novel setting of geometric knowledge reasoning necessitates new LM abilities beyond existing atomic/linear multi-hop QA, such as backtracking, verifying facts and constraints, reasoning with uncertainty, and more.

Knowledge Card: Filling LLMs' Knowledge Gaps with Plug-in Specialized Language Models

2 code implementations17 May 2023 Shangbin Feng, Weijia Shi, Yuyang Bai, Vidhisha Balachandran, Tianxing He, Yulia Tsvetkov

Ultimately, Knowledge Card framework enables dynamic synthesis and updates of knowledge from diverse domains.

Retrieval

FactKB: Generalizable Factuality Evaluation using Language Models Enhanced with Factual Knowledge

1 code implementation14 May 2023 Shangbin Feng, Vidhisha Balachandran, Yuyang Bai, Yulia Tsvetkov

We propose FactKB, a simple new approach to factuality evaluation that is generalizable across domains, in particular with respect to entities and relations.

News Summarization

Assessing Language Model Deployment with Risk Cards

2 code implementations31 Mar 2023 Leon Derczynski, Hannah Rose Kirk, Vidhisha Balachandran, Sachin Kumar, Yulia Tsvetkov, M. R. Leiser, Saif Mohammad

However, there is no risk-centric framework for documenting the complexity of a landscape in which some risks are shared across models and contexts, while others are specific, and where certain conditions may be required for risks to manifest as harms.

Language Modelling Text Generation

Language Generation Models Can Cause Harm: So What Can We Do About It? An Actionable Survey

no code implementations14 Oct 2022 Sachin Kumar, Vidhisha Balachandran, Lucille Njoo, Antonios Anastasopoulos, Yulia Tsvetkov

Recent advances in the capacity of large language models to generate human-like text have resulted in their increased adoption in user-facing settings.

Language Modelling Text Generation

Unsupervised Keyphrase Extraction via Interpretable Neural Networks

1 code implementation15 Mar 2022 Rishabh Joshi, Vidhisha Balachandran, Emily Saldanha, Maria Glenski, Svitlana Volkova, Yulia Tsvetkov

Keyphrase extraction aims at automatically extracting a list of "important" phrases representing the key concepts in a document.

Keyphrase Extraction Topic Classification

DialoGraph: Incorporating Interpretable Strategy-Graph Networks into Negotiation Dialogues

2 code implementations ICLR 2021 Rishabh Joshi, Vidhisha Balachandran, Shikhar Vashishth, Alan Black, Yulia Tsvetkov

To successfully negotiate a deal, it is not enough to communicate fluently: pragmatic planning of persuasive negotiation strategies is essential.

Response Generation

Simple and Efficient ways to Improve REALM

no code implementations EMNLP (MRQA) 2021 Vidhisha Balachandran, Ashish Vaswani, Yulia Tsvetkov, Niki Parmar

Dense retrieval has been shown to be effective for retrieving relevant documents for Open Domain QA, surpassing popular sparse retrieval methods like BM25.

Retrieval

StructSum: Summarization via Structured Representations

1 code implementation EACL 2021 Vidhisha Balachandran, Artidoro Pagnoni, Jay Yoon Lee, Dheeraj Rajagopal, Jaime Carbonell, Yulia Tsvetkov

To this end, we propose incorporating latent and explicit dependencies across sentences in the source document into end-to-end single-document summarization models.

Abstractive Text Summarization Decoder +2

Differentiable Reasoning over a Virtual Knowledge Base

1 code implementation ICLR 2020 Bhuwan Dhingra, Manzil Zaheer, Vidhisha Balachandran, Graham Neubig, Ruslan Salakhutdinov, William W. Cohen

In particular, we describe a neural module, DrKIT, that traverses textual data like a KB, softly following paths of relations between mentions of entities in the corpus.

Re-Ranking

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