1 citations · 1 across the 9 of their papers we have counts for
10 papers
Who is the Agent to Blame? Localizing Faithfulness and Citation Mistakes in Agentic Deep Research
Eran Hirsch, David Wan, Han Wang +3
Deep research (DR) systems produce long-form cited reports by orchestrating multiple agents that search and synthesize information from the web. Citations are the primary mechanism…
MERRIN: A Benchmark for Multimodal Evidence Retrieval and Reasoning in Noisy Web Environments
Han Wang, David Wan, Hyunji Lee +6
Motivated by the underspecified, multi-hop nature of search queries and the multimodal, heterogeneous, and often conflicting nature of real-world web results, we introduce MERRIN (…
Multimodal Fact-Level Attribution for Verifiable Reasoning
David Wan, Han Wang, Ziyang Wang +3
Multimodal large language models (MLLMs) are increasingly used for real-world tasks involving multi-step reasoning and long-form generation, where reliability requires grounding mo…
PrefixNLI: Detecting Factual Inconsistencies as Soon as They Arise
Sapir Harary, Eran Hirsch, Aviv Slobodkin +3
Natural Language Inference (NLI) models have been used in various ways to improve the factuality of LLM outputs. This is typically done by applying an NLI model to judge whether th…
GenerationPrograms: Fine-grained Attribution with Executable Programs
David Wan, Eran Hirsch, Elias Stengel-Eskin +2
Recent large language models (LLMs) achieve impressive performance in source-conditioned text generation but often fail to correctly provide fine-grained attributions for their out…
LAQuer: Localized Attribution Queries in Content-grounded Generation
Eran Hirsch, Aviv Slobodkin, David Wan +3
Grounded text generation models often produce content that deviates from their source material, requiring user verification to ensure accuracy. Existing attribution methods associa…
QAPyramid: Fine-grained Evaluation of Content Selection for Text Summarization
Shiyue Zhang, David Wan, Arie Cattan +3
How to properly conduct human evaluations for text summarization is a longstanding challenge. The Pyramid human evaluation protocol, which assesses content selection by breaking th…
On Positional Bias of Faithfulness for Long-form Summarization
David Wan, Jesse Vig, Mohit Bansal +1
Large Language Models (LLMs) often exhibit positional bias in long-context settings, under-attending to information in the middle of inputs. We investigate the presence of this bia…
Localizing Factual Inconsistencies in Attributable Text Generation
Arie Cattan, Paul Roit, Shiyue Zhang +5
There has been an increasing interest in detecting hallucinations in model-generated texts, both manually and automatically, at varying levels of granularity. However, most existin…
The Power of Summary-Source Alignments
Ori Ernst, Ori Shapira, Aviv Slobodkin +5
Multi-document summarization (MDS) is a challenging task, often decomposed to subtasks of salience and redundancy detection, followed by text generation. In this context, alignment…