Papers by Sujith Ravi
RCI: A Score for Evaluating Global and Local Reasoning in Multimodal Benchmarks (2025.emnlp-industry)
Copied to clipboard
Amit Agarwal, Hitesh Laxmichand Patel, Srikant Panda, Hansa Meghwani, Jyotika Singh, Karan Dua, Paul Li, Tao Sheng, Sujith Ravi, Dan Roth
| Challenge: | Existing evaluation methods do not explicitly measure this distinction, hindering effective dataset curation and real-world focused model development. |
| Approach: | They introduce a region-based score to quantify a dataset's reliance on global versus local visual information. |
| Outcome: | The proposed model-based score systematically compares model performance on image patches versus full images to determine if tasks require holistic image understanding or can be solved with partial or localized visual cues. |
GoEmotions: A Dataset of Fine-Grained Emotions (2020.acl-main)
Copied to clipboard
| Challenge: | Existing datasets for language-based emotion classification are limited and small . existing datasets lack quality annotations for many different emotion categories . |
| Approach: | They propose to use a large manually annotated dataset to study emotion expressions . they conduct transfer learning experiments with existing emotion benchmarks to test their model . |
| Outcome: | The proposed model achieves an average F1-score of .46, leaving room for improvement. |
Low-Dimensional Hyperbolic Knowledge Graph Embeddings (2020.acl-main)
Copied to clipboard
| Challenge: | Existing methods for predicting missing facts do not account for hierarchical and logical patterns in KGs. |
| Approach: | They propose a class of hyperbolic KG embedding models that capture hierarchical and logical patterns. |
| Outcome: | Experimental results show that the proposed method improves by 6.1% in mean reciprocal rank in low dimensions over previous methods. |
Efficiently Summarizing Text and Graph Encodings of Multi-Document Clusters (2021.naacl-main)
Copied to clipboard
| Challenge: | Abstractive multi-document summarization (MDS) is a task that has seen advances with the introduction of large-scale datasets and powerful Transformer-based models. |
| Approach: | They propose an efficient graph-enhanced approach to multi-document summarization with an encoder-decoder Transformer model. |
| Outcome: | The proposed model scales to large input documents and improves on a multi-document dataset. |
On-device Structured and Context Partitioned Projection Networks (P19-1)
Copied to clipboard
| Challenge: | A challenge in on-device text classification is to build highly accurate models that fit in small memory footprint and have low latency. |
| Approach: | They propose an on-device neural network which learns compact projection vectors from raw text using structured and context-dependent partition projections. |
| Outcome: | The proposed model outperforms baseline models and surpasses RNN, CNN and BiLSTM models on dialog act and intent prediction. |
Aligning LLMs for Multilingual Consistency in Enterprise Applications (2025.emnlp-industry)
Copied to clipboard
| Challenge: | Large language models (LLMs) remain unreliable for global enterprise applications due to performance gaps between high-resource and mid/low-resourced languages . |
| Approach: | They propose a batch-wise alignment strategy that aligns model outputs across languages . this method improves non-English accuracy by up to 23.9% without compromising English performance . |
| Outcome: | The proposed approach improves non-English accuracy by up to 23.9% without compromising English performance, model reasoning, or retrieval quality. |
Evaluating the Tradeoff Between Abstractiveness and Factuality in Abstractive Summarization (2023.findings-eacl)
Copied to clipboard
| Challenge: | Abstractive summarization models generate fluent and well-formed output but lack semantic faithfulness, or factuality, with respect to the input documents. |
| Approach: | They propose new factuality metrics that adjust for the degree of abstractiveness . they propose to visualize the rates of change in factual as we gradually increase abstractiveity . |
| Outcome: | The proposed models generate fluent and well-formed summaries but lack semantic faithfulness, or factuality, with respect to the input documents. |
Can LLMs Narrate Tabular Data? An Evaluation Framework for Natural Language Representations of Text-to-SQL System Outputs (2025.emnlp-industry)
Copied to clipboard
| Challenge: | Text-to-SQL technology bridges natural language (NL) questions and database querying. |
| Approach: | They propose a method for evaluating LLM-generated NLRs using Combo-Eval and a dataset for NLR benchmarking. |
| Outcome: | The proposed method reduces LLM calls by 25-61% and improves performance across scenarios with and without ground truth references. |
Self-Governing Neural Networks for On-Device Short Text Classification (D18-1)
Copied to clipboard
| Challenge: | Existing deep neural networks have a tiny memory footprint and low computational capacity compared to high performance computing systems such as CPUs, GPUs and TPUs on the cloud. |
| Approach: | They propose on-device self-governing neural networks which learn compact projection vectors with local sensitive hashing. |
| Outcome: | The proposed models perform better on dialog act classification tasks while maintaining high accuracy. |
PCRI: Measuring Context Robustness in Multimodal Models for Enterprise Applications (2025.emnlp-industry)
Copied to clipboard
Hitesh Laxmichand Patel, Amit Agarwal, Srikant Panda, Hansa Meghwani, Karan Dua, Paul Li, Tao Sheng, Sujith Ravi, Dan Roth
| Challenge: | Existing evaluation metrics for Multimodal Large Language Models (MLLMs) are inadequate to assess their robustness to irrelevant or distracting visual context. |
| Approach: | They propose a patch-context-robustness index to measure MLLMs' robustness to visual context variations. |
| Outcome: | The proposed score measures the robustness of MLLMs to visual contexts across 15 vision-language benchmarks. |
Transferable Neural Projection Representations (N19-1)
Copied to clipboard
| Challenge: | Neural word embeddings require lookup and a large memory footprint making it hard to deploy on-device. |
| Approach: | They propose a skip-gram based architecture coupled with Locality-Sensitive Hashing projections to learn efficient dynamically computable representations. |
| Outcome: | The proposed model performs better than previous models on multiple NLP tasks. |
ProFormer: Towards On-Device LSH Projection Based Transformers (2021.eacl-main)
Copied to clipboard
| Challenge: | ProFormer is a projection based transformer architecture that is faster and lighter making it suitable to deploy to memory constraint devices such as mobile phones, watches and IoT. |
| Approach: | They propose a projection based transformer architecture that generates word representations on-the-fly without embedding lookup tables and a local projection attention layer that transforms the input sequence of N LSH word projections into a sequence of K representations. |
| Outcome: | The proposed architecture reduces memory footprint from 92.16 MB to 1.7 KB and requires 16x less computation overhead making it suitable to deploy to memory constraint devices and preserve user privacy. |
MT-OSC: Path for LLMs that Get Lost in Multi-Turn Conversation (2026.findings-acl)
Copied to clipboard
Jyotika Singh, Fang Tu, Miguel Ballesteros, Weiyi Sun, Sandip Ghoshal, Michelle Yuan, Yassine Benajiba, Sujith Ravi, Dan Roth
| Challenge: | Large language models suffer performance degradation when user instructions and context are distributed over multiple conversational turns. |
| Approach: | They propose a framework that condenses chat history in the background without disrupting the user experience. |
| Outcome: | The proposed framework reduces token counts by up to 72% in 10-turn dialogues while remaining robust to distractors and irrelevant turns. |
On-Device Text Representations Robust To Misspellings via Projections (2021.eacl-main)
Copied to clipboard
| Challenge: | Recent advances in Locality-Sensitive Hashing (LSH)-based projection networks have demonstrated state-of-the-art performance in various classification tasks without explicit word embedding lookup tables by computing on-the fly text representations. |
| Approach: | They propose to use locality-sensitive hashing to compute on-the-fly text representations without explicit word embedding tables. |
| Outcome: | The proposed classifiers are more robust to common misspellings and perturbations of the input text compared to biLSTMs and fine-tuned BERT based methods. |
LAD-RAG: Layout-aware Dynamic RAG for Visually-Rich Document Understanding (2026.acl-long)
Copied to clipboard
Zhivar Sourati, Zheng Wang, Marianne Menglin Liu, Yazhe Hu, Mengqing Guo, Sujeeth Bharadwaj, Kyu J. Han, Tao Sheng, Sujith Ravi, Morteza Dehghani, Dan Roth
| Challenge: | Conventional retrieval-augmented generation (RAG) methods encode content in isolated chunks during ingestion, losing structural and cross-page dependencies, and retrieve a fixed number of pages at inference. |
| Approach: | They propose a Layout-Aware Dynamic RAG framework that encodes content in isolated chunks during ingestion and retrieves a fixed number of pages at inference. |
| Outcome: | Experiments on MMLongBench-Doc, LongDocURL, DUDE, and MP-DoxVQA show that LAD-RAG improves retrieval, achieving over 90% perfect recall on average without any top-k tuning, and outperforming baseline retrievers by up to 20% in recall at comparable noise levels. |
PAR2-RAG: Planned Active Retrieval and Reasoning for Multi-Hop Question Answering (2026.acl-industry)
Copied to clipboard
| Challenge: | Multi-hop question answering is a practical bottleneck in industry applications . large language models (LLMs) fail frequently when evidence coverage is incomplete or reasoning trajectories drift . |
| Approach: | They propose a training-free two-stage framework that separates coverage from commitment . it performs breadth-first anchoring to build a high-recall evidence frontier . compared with IRCoT, it achieves 23.5% higher answer accuracy . |
| Outcome: | The proposed framework outperforms strong baselines in MHQA benchmarks and achieves 23.5% higher answer accuracy and 10.5% NDCG gains in retrieval quality. |
PRADO: Projection Attention Networks for Document Classification On-Device (D19-1)
Copied to clipboard
| Challenge: | Recent advances in deep learning have improved the performance of on-device neural networks for long text classification. |
| Approach: | They propose a projection attention neural network PRADO that combines trainable projections with attention and convolutions to train tiny neural networks that achieve high performance on multiple long document classification tasks. |
| Outcome: | The proposed model achieves high performance on multiple long document classification tasks while maintaining compact size. |
A2N: Attending to Neighbors for Knowledge Graph Inference (P19-1)
Copied to clipboard
| Challenge: | Existing knowledge graph completion methods learn a fixed embedding for every entity, which is suboptimal as it requires memorizing and generalizing to all possible entity relationships. |
| Approach: | They propose a method which learns query-dependent representations of entities by combining relevant neighborhood of an entity. |
| Outcome: | The proposed model performs competitively or better than existing state-of-the-art models for knowledge graph completion. |
JTPRO: A Joint Tool–Prompt Reflective Optimization Framework for Language Agents (2026.findings-acl)
Copied to clipboard
Sandip Ghoshal, Anshul Mittal, Jyotika Singh, Miguel Ballesteros, Weiyi Sun, Fang Tu, Shailender Singh, Yassine Benajiba, Fahad Shah, Sujeeth Bharadwaj, Sujith Ravi, Dan Roth
| Challenge: | Large language model agents struggle with ambiguous tool descriptions and underspecified tool schemas that ignore tool-specific nuances. |
| Approach: | They propose a framework for improving tool-calling reliability in trace-supervised settings by rolling out-driven reflection. |
| Outcome: | The proposed framework outperforms baselines and reflective prompt optimizers by 5%–20% on OSR. |
Self-Governing Neural Networks for On-Device Short Text Classification (D18-1)
Copied to clipboard
| Challenge: | Existing deep neural networks have a tiny memory footprint and low computational capacity compared to high performance computing systems such as CPUs, GPUs and TPUs on the cloud. |
| Approach: | They propose on-device self-governing neural networks which learn compact projection vectors with local sensitive hashing. |
| Outcome: | The proposed models perform better on dialog act classification tasks while maintaining high accuracy. |
Do Image–Text Metrics Respect Semantic Invariances? (2026.findings-acl)
Copied to clipboard
Amit Agarwal, Hitesh Laxmichand Patel, Meizhu Liu, Jyotika Singh, Karan Dua, Hansa Meghwani, Matthew Rowe, M. Avendi, Yassi Abbasi, Tao Sheng, Sujith Ravi, Dan Roth
| Challenge: | Reference-free image–to–text evaluators are now standard for scoring image–caption alignment, yet it is unclear whether they respect semantic invariances. |
| Approach: | They propose an invariance probe on five popular evaluators under semantics-preserving perturbations along three axes: spatial edits, object changes, and socio-linguistic framing. |
| Outcome: | The proposed invariance probe shows that spatial edits and simple phrasing changes shift scores by ()6% on average and cause ranking flips in up to (),37% of cases. |
ProSeqo: Projection Sequence Networks for On-Device Text Classification (D19-1)
Copied to clipboard
| Challenge: | ProSeqo is a novel on-device sequence model for text classification . it uses dynamic recurrent projections without the need to store or look up pre-trained embeddings. |
| Approach: | They propose a novel on-device sequence model for text classification using recurrent projections that uses dynamic recursion projections without the need to store or look up any pre-trained embeddings. |
| Outcome: | The proposed model outperforms state-of-the-art neural and on-device approaches for short and long text classification tasks while maintaining low memory footprint and high accuracy. |