Papers by Ayush Jain
Imbalanced Gradients in RL Post-Training of Multi-Task LLMs (2026.findings-eacl)
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Runzhe Wu, Ankur Samanta, Ayush Jain, Scott Fujimoto, Jeongyeol Kwon, Ben Kretzu, Youliang Yu, Kaveh Hassani, Boris Vidolov, Yonathan Efroni
| Challenge: | Large-gradient tasks can achieve similar or even much lower learning gains than small-grading ones. |
| Approach: | They show that large-gradient tasks can achieve lower learning gains than small-grading ones . large-grade tasks can accomplish similar or even lower learning gain than small grade ones if they are large . |
| Outcome: | The proposed approach fails when certain tasks produce larger gradients . Large-gradient tasks can achieve lower learning gains than small-gradent ones . |
The Bull and the Bear: Summarizing Stock Market Discussions (2022.lrec-1)
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| Challenge: | a dataset of 7888 reddit posts and 400 posts is used to summarize stock market topics. |
| Approach: | They curate discussions on social media platforms and construct an abstractive summarization dataset. |
| Outcome: | The proposed dataset consists of 7888 Reddit posts and summaries for 400 posts . it is robustly evaluated and will be made publicly available . |
Does Social Pressure Drive Persuasion in Online Fora? (2021.emnlp-main)
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| Challenge: | Using social features, we hypothesize that comments from the ambient community can either affirm the original view or implicitly exert pressure to change it. |
| Approach: | They propose a structured model to capture the ambient community’s sentiment towards the discussion and its effect on persuasion. |
| Outcome: | The proposed model captures the ambient community’s sentiment towards the discussion and its effect on persuasion. |
Scaling Laws and Efficient Inference for Ternary Language Models (2025.acl-long)
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Tejas Vaidhya, Ayush Kaushal, Vineet Jain, Francis Couture-Harpin, Prashant Shishodia, Majid Behbahani, Yuriy Nevmyvaka, Irina Rish
| Challenge: | Large language models (LLMs) are increasingly used across research and industry applications, yet their inference efficiency remains a challenge. |
| Approach: | They propose ternary language models that employ quantization-aware training to significantly reduce memory requirements. |
| Outcome: | The proposed ternary language models demonstrate sustained performance gains at scale. |
COGMEN: COntextualized GNN based Multimodal Emotion recognitioN (2022.naacl-main)
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| Challenge: | During a conversation, a person’s emotions are influenced by the other speaker’s utterances and their own emotional state over the utterrances. |
| Approach: | They propose a Graph Neural Network based Multi-modal Emotion recognitioN system that leverages local and global information in a conversation. |
| Outcome: | The proposed system gives state-of-the-art results on IEMOCAP and MOSEI datasets and detailed ablation experiments show the importance of modeling information at both levels. |
Entity Exchange in the Wild: A Diagnostic Study of LLM Based Real-World Conversational Entity Extraction (2026.acl-industry)
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| Challenge: | Prior work has examined the impact of transcription noise and cross-turn reasoning, but it has not systematically analyzed how entity-exchange phenomena themselves shape extraction performance. |
| Approach: | They evaluate 16 large language models on 6,387 real-world customer–agent conversations spanning 12 entity types across numeric, alphanumeric, temporal, and free-text categories. |
| Outcome: | The proposed model improves on the extracted entities across all three axes yielding average gains of up to 6.4% across models. |
DeepResearch Retail: Benchmarking Tool-Augmented Deep Research in the E-Commerce Domain (2026.acl-industry)
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| Challenge: | Existing DR systems are largely web-centric and do not incorporate structured, domain-specific, and personalized information accessible through internal API tools. |
| Approach: | They propose a framework grounded in real-world e-commerce data for assessing Deep Research with tools in realistic commercial settings. |
| Outcome: | The proposed framework evaluates factual faithfulness and multidimensional response quality when reasoning over heterogeneous web and internal data sources. |