Papers by Hritik Bansal
How much complexity does an RNN architecture need to learn syntax-sensitive dependencies? (2020.acl-srw)
Copied to clipboard
| Challenge: | Long-term memory (LSTM) networks are capable of encapsulating long-range dependencies . but simple recurrent networks (SRNs) have been less successful at capturing long-term dependencies and loci of grammatical errors in an unsupervised setting. |
| Approach: | They propose a new architecture that incorporates the decaying nature of neuronal activations and models the excitatory and inhibitory connections in a population of neurons. |
| Outcome: | The proposed architecture shows competitive performance relative to LSTMs on subject-verb agreement, sentence grammaticality, and language modeling tasks. |
GeoMLAMA: Geo-Diverse Commonsense Probing on Multilingual Pre-Trained Language Models (2022.emnlp-main)
Copied to clipboard
| Challenge: | Recent work shows that Pre-trained Language Models store relational knowledge and utilize it for performing downstream tasks. |
| Approach: | They propose a benchmark dataset for probing the diversity of relational knowledge in multilingual PLMs. |
| Outcome: | The proposed dataset contains 3125 prompts in English, Chinese, Hindi, Persian, and Swahili . larger multilingual PLMs variants do not store geo-diverse concepts better than its smaller variant . |
BIG-Bench Extra Hard (2025.acl-long)
Copied to clipboard
Mehran Kazemi, Bahare Fatemi, Hritik Bansal, John Palowitch, Chrysovalantis Anastasiou, Sanket Vaibhav Mehta, Lalit K Jain, Virginia Aglietti, Disha Jindal, Peter Chen, Nishanth Dikkala, Gladys Tyen, Xin Liu, Uri Shalit, Silvia Chiappa, Kate Olszewska, Yi Tay, Vinh Q. Tran, Quoc V Le, Orhan Firat
| Challenge: | Current benchmarks for large language model reasoning focus on math and coding abilities, leaving a gap in evaluating broader reasoning proficiencies. |
| Approach: | They propose a benchmark to evaluate general reasoning in large language models . they use BIG-Bench and its harder version BIG-Benefit Hard to assess general reasoning . |
| Outcome: | The new benchmark pushes the boundaries of LLM reasoning evaluation. |
Rethinking the Role of Scale for In-Context Learning: An Interpretability-based Case Study at 66 Billion Scale (2023.acl-long)
Copied to clipboard
| Challenge: | 70% of attention heads and 20% of the feed forward networks can be removed with minimal decline in task performance. |
| Approach: | They propose to investigate whether in-context learning is not uniform across all components of a large language model. |
| Outcome: | The proposed model can remove 70% of attention heads and 20% of feed forward networks with minimal decline in task performance. |
Dynosaur: A Dynamic Growth Paradigm for Instruction-Tuning Data Curation (2023.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods for instruction tuning do not include associating instructions with existing datasets. |
| Approach: | They propose a dynamic growth paradigm for the automatic curation of instruction-tuning data . they use existing datasets to automatically construct instruction-uning datasets . |
| Outcome: | The proposed model reduces the API cost for generating instructions and provides high-quality data. |
How well can Text-to-Image Generative Models understand Ethical Natural Language Interventions? (2022.emnlp-main)
Copied to clipboard
| Challenge: | Text-to-image generative models can generate high-quality photo-realistic images conditional on natural language text descriptions in a zero-shot fashion. |
| Approach: | They propose an Ethical NaTural Language Interventions in Text-to-Image GENeration benchmark dataset to evaluate the change in image generation conditional on ethical interventions across three social axes – gender, skin color, and culture. |
| Outcome: | The proposed model generations cover diverse social groups while preserving image quality. |
Comparing Bad Apples to Good Oranges Aligning Large Language Models via Joint Preference Optimization (2025.findings-acl)
Copied to clipboard
| Challenge: | Recent studies have shown that acquiring human preferences by comparing generations is not effective for large language models. |
| Approach: | They propose a preference optimization objective that elicits preferences jointly over the instruction-response pairs. |
| Outcome: | The proposed approach outperforms prior preference optimizations by 5.2% and 3.3% in summarization and open-ended dialogue datasets. |
Improving Event Definition Following For Zero-Shot Event Detection (2024.acl-long)
Copied to clipboard
Zefan Cai, Po-Nien Kung, Ashima Suvarna, Mingyu Ma, Hritik Bansal, Baobao Chang, P. Jeffrey Brantingham, Wei Wang, Nanyun Peng
| Challenge: | Existing approaches on zero-shot event detection train models on datasets annotated with known event types and prompt them with unseen event definitions. |
| Approach: | They propose to train models to better follow event definitions by using an automatic generated Diverse Event Definition dataset. |
| Outcome: | The proposed model outperforms existing models on three open benchmarks on zero-shot event detection. |