Papers by Harman Singh
Cross-Lingual Multi-Hop Knowledge Editing (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Prior work on knowledge editing in monolingual settings focused on a single language, but there are significant gaps in performance between the two settings. |
| Approach: | They propose a cross-lingual multi-hop knowledge editing paradigm for measuring and analyzing the performance of various SoTA knowledge editing techniques in a multilingual setup. |
| Outcome: | The proposed system improves on previous methods in a cross-lingual setting and in English. |
Coarse-to-Fine Contrastive Learning in Image-Text-Graph Space for Improved Vision-Language Compositionality (2023.emnlp-main)
Copied to clipboard
| Challenge: | Recent studies have highlighted severe limitations of contrastive learning models in their ability to perform compositional reasoning over objects, attributes, and relations. |
| Approach: | They propose a graph decomposition framework and negative mining techniques to improve attribute binding and relation understanding of scene graphs. |
| Outcome: | The proposed approach improves attribute binding, relation understanding, generalization, and productivity on multiple benchmarks. |
IndicGenBench: A Multilingual Benchmark to Evaluate Generation Capabilities of LLMs on Indic Languages (2024.acl-long)
Copied to clipboard
| Challenge: | IndicGenBench is the largest benchmark for evaluating large language models on user-facing generation tasks across a diverse set of 29 Indic languages . |
| Approach: | They evaluate large language models on user-facing generation tasks across 29 languages . they use human curation to provide multi-way parallel evaluation data for many under-represented languages a github repository . |
| Outcome: | IndicGenBench is the largest benchmark for evaluating LLMs on user-facing generation tasks across a diverse set of 29 Indic languages covering 13 scripts and 4 language families. |
FaiRR: Faithful and Robust Deductive Reasoning over Natural Language (2022.acl-long)
Copied to clipboard
| Challenge: | Currently, black-box models generate both the proof graph and intermediate inferences within the same model and thus may be unfaithful. |
| Approach: | They propose a transformer-based model that can perform deductive reasoning on a logical rulebase containing rules and statements written in natural language. |
| Outcome: | The proposed model is robust to language perturbations and faster at inference than previous models on existing reasoning datasets. |
Image Manipulation via Multi-Hop Instructions - A New Dataset and Weakly-Supervised Neuro-Symbolic Approach (2023.emnlp-main)
Copied to clipboard
Harman Singh, Poorva Garg, Mohit Gupta, Kevin Shah, Ashish Goswami, Satyam Modi, Arnab Mondal, Dinesh Khandelwal, Dinesh Garg, Parag Singla
| Challenge: | Recent studies have shown that neuro-symbolic models lack interpretability and are not robust to noise. |
| Approach: | They propose to extend Neuro Symbolic Concept Learning (NSCL) which has been quite effective for the task of Visual Question Answering (VQA) they create a new dataset for the image manipulation task and demonstrate that NeuroSIM is highly competitive with or beats SOTA baselines that make use of supervised data for manipulation. |
| Outcome: | The proposed system performs complex multi-hop reasoning over multi-object scenes and only requires weak supervision in the form of annotated data for VQA. |