Papers by Paramveer Dhillon
ExAnte: A Benchmark for Ex-Ante Inference in Large Language Models (2026.eacl-long)
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| Challenge: | Large language models (LLMs) struggle with ex-ante reasoning—making inferences or predictions without access to future information. |
| Approach: | They propose a benchmark that assesses LLMs’ ex-ante inference ability across four tasks: stock prediction, question answering, Wikipedia event generation, and scientific publication generation. |
| Outcome: | The proposed benchmark assesses LLMs’ ex-ante inference ability across four tasks. |
Dual Debiasing for Noisy In-Context Learning for Text Generation (2025.findings-acl)
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| Challenge: | Existing methods detect noisy annotations by ranking local perplexities, but this assumption breaks down when the noise ratio is high and many demonstrations are flawed. |
| Approach: | They propose a method that uses synthesized neighbors to explicitly correct perplexity estimates, yielding a robust Sample Cleanliness Score. |
| Outcome: | The proposed method is comparable to a fully clean demonstration corpus and performs well even with noise ratios as high as 0.8. |
Causal Inference for Human-Language Model Collaboration (2024.naacl-long)
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| Challenge: | In this paper, we examine the collaborative dynamics between humans and language models where the interaction involves LMs proposing text segments and humans editing or responding to these segments. |
| Approach: | They propose a causal estimand to estimate the incremental stylistic effect (ISE) of various interaction strategies in dynamic human-LM collaborations. |
| Outcome: | The proposed estimand reduces confounding and significantly improves counterfactual estimation over a set of competitive baselines. |