Papers by Adit Krishnan
Learning from Natural Language Explanations for Generalizable Entity Matching (2024.emnlp-main)
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| Challenge: | Entity matching is the task of linking records from different sources that refer to the same real-world entity. |
| Approach: | They propose to "distill" LLM reasoning into smaller entity matching models via natural language explanations. |
| Outcome: | The proposed model distillation approach achieves strong performance on out-of-domain generalization tests (10.85% F-1). |
Audience-Centric Natural Language Generation via Style Infusion (2022.findings-emnlp)
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| Challenge: | Existing approaches to text style transfer (TST) with large volumes of parallel or non-parallel data are limiting for two reasons: it is difficult to collect large volumes and some stylistic objectives are hard to define without audience feedback. |
| Approach: | They propose a task of style infusion - infusing stylistic preferences of audiences into pretrained language generation models by leveraging pairwise human judgments to bootstrap a style analysis model and augment a seed set of judgments. |
| Outcome: | The proposed method generates compelling stylized examples with generic text prompts while balancing fluency and style adoption. |
CEV-LM: Controlled Edit Vector Language Model for Shaping Natural Language Generations (2024.eacl-long)
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| Challenge: | Existing control approaches primarily adjust the semantic (e.g., emotion, topics), structural (e-speech, parts-of-seech), and lexical (el-s-sp-s) properties of text, but are insufficient to accomplish complex objectives such as pacing which control the complexity and readability of the text. |
| Approach: | They propose a lightweight semi-autoregressive language model that uses edit vectors to control three complementary metrics that quantify the shape of text. |
| Outcome: | The proposed model provides significantly more targeted and precise control of speed, volume, and circuitousness while using less training data, and containing fewer parameters. |
BPID: A Benchmark for Personal Identity Deduplication (2024.emnlp-industry)
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Runhui Wang, Yefan Tao, Adit Krishnan, Luyang Kong, Xuanqing Liu, Yuqian Deng, Yunzhao Yang, Henrik Johnson, Andrew Borthwick, Shobhit Gupta, Aditi Gundlapalli, Davor Golac
| Challenge: | Data deduplication is a critical task in data management and mining, focused on consolidating duplicate records that refer to the same entity. |
| Approach: | They propose to use a dataset with 1,000,000 unlabeled synthetic PII profiles and a subset of 10,000 pairs curated and labeled as matches or non-matches. |
| Outcome: | The proposed datasets contain synthetic profiles built from publicly available sources that do not represent real individuals. |