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Vidhisha Balachandran

Vidhisha Balachandran has 4 papers in our library, in 2021–2024, most of them on Evaluation & benchmarks, Summarization, Text generation; often with Yulia Tsvetkov, Akari Asai, Antonios Anastasopoulos.

4 papers in our library · 2021–2024

OpenAlex · ORCID · Wikidata · h-index 10 · 71 works · 540 citations OpenAlex, 2026-10-14

📄 4 works in our library · 🔗 593 citations in our library · 6 more in our library without a page yet · ⭐ Paper Star rank #2851

The profile is the library's match of the name; two people with one name are not always told apart.

Elsewhere: Google Scholar · Semantic Scholar · dblp · ACL Anthology

 Affiliations

🏢MicrosoftRedmond · United States2024–2026 · 9 paperscurrent🎓Carnegie Mellon UniversityPittsburgh · United States2018–2024 · 36 papers🎓University of WashingtonSeattle · United States2023–2025 · 2 papers🔬Allen Institute for Artificial IntelligenceSeattle · United States2022–2023 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 2 more institutions on single bylines are not listed.

 Concepts, shelves and topics

Of its works in our library: the number of works on each and its rating among them.

📚 Shelves 10 Computer Science · 4 works Artificial Intelligence · 4 works Arts and Humanities · 3 works Language and Linguistics · 3 works Social Sciences · 2 works Medicine · 1 work Sociology and Political Science · 1 work Safety Research · 1 work Computer Vision and Pattern Recognition · 1 work Health Informatics · 1 work

🧭 Topics 4 Evaluation & benchmarks · 2 works Summarization · 1 work Text generation · 1 work Reasoning · 1 work

 Works by year

2020s

20242 papers20231 paper20211 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Understanding Factuality in Abstractive Summarization with FRANK: A Benchmark for Factuality Metrics (2021 · 418 citations)
    Neural abstractive summarizers write fluent text that often misstates facts from the source. Existing factuality metrics could not be compared because there was no shared benchmark, and they all reduce factuality to a yes/no label for a whole summary.
  2. Fine-grained Hallucination Detection and Editing for Language Models (2024 · 96 citations)
    Language models frequently state false facts. Most existing checkers give only a yes/no verdict for an entire response, so every error is treated the same way. The authors argue that factual mistakes come in distinct kinds, each requiring a different amount of verification effort, and they organize these kinds into a…
  3. Language Generation Models Can Cause Harm: So What Can We Do About It? An Actionable Survey (2023 · 87 citations)
    Much has been written about the dangers of large text generators, but practical ways to counter them were scattered across separate research communities.
  4. MediQ: Question-Asking LLMs and a Benchmark for Reliable Interactive Clinical Reasoning (2024 · 70 citations)
    Most medical QA benchmarks give the model the whole case at once. Real consultations are different: patients often leave out details, and a clinician has to ask follow-up questions before deciding. The authors argue that LLMs are tuned to answer no matter what, which makes them unreliable when the context is…

Also in our library

6 more works of theirs are in our library without a page here yet; they will appear as we publish them.

 Co-authors here

9 co-authors with a page, the most papers together first.

 Cited by

Papers of our library cite their works 593 times (of the 141,840 papers whose bibliographies we read); 46 citing papers with a page here →.

Updated

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