Papers by Abhik Jana
TripTide: A Benchmark for Adaptive Travel Planning under Disruptions (2026.findings-acl)
Copied to clipboard
Priyanshu Karmakar, Soumyabrata Chaudhuri, Shubhojit Mallick, Manish Gupta, Abhik Jana, Shreya Ghosh
| Challenge: | Recent work has shown the promise of Large Language Models (LLMs) for personalized, constraint-aware travel itinerary generation, but real-world travel often involves disruptions such as transit cancellations, weather-related closures, or overbooked attractions. |
| Approach: | They propose a benchmark to evaluate the ability of Large Language Models (LLMs) to revise travel itineraries under realistic disruptions. |
| Outcome: | The proposed benchmark evaluates the ability of Large Language Models (LLMs) to revise travel itineraries under real-world disruption scenarios. |
Can Network Embedding of Distributional Thesaurus Be Combined with Word Vectors for Better Representation? (N18-1)
Copied to clipboard
| Challenge: | Distributed representations of words learned from text have proved to be successful in various natural language processing tasks. |
| Approach: | They propose to embed a distributional thesaurus network into dense word vectors and compare them to state-of-the-art word representations. |
| Outcome: | The proposed representations improve performance against state-of-the-art word representations even without handcrafted lexical resources. |
Network Features Based Co-hyponymy Detection (L18-1)
Copied to clipboard
| Challenge: | Existing methods to detect lexical relations have been used to identify them in both supervised and unsupervised ways. |
| Approach: | They propose to use distributional semantic models to detect co-hyponymy relation with high accuracy and various network measures to perform better or at par with the state-of-the-art models. |
| Outcome: | The proposed model performs better or at par with the state-of-the-art models. |
TripCraft: A Benchmark for Spatio-Temporally Fine Grained Travel Planning (2025.acl-long)
Copied to clipboard
Soumyabrata Chaudhuri, Pranav Purkar, Ritwik Raghav, Shubhojit Mallick, Manish Gupta, Abhik Jana, Shreya Ghosh
| Challenge: | Existing benchmarks such as TravelPlanner and TravelPlann+ rely on semi-synthetic data and lack key real-world constraints. |
| Approach: | They propose a spatio-temporally coherent travel planning dataset incorporating real-world constraints, including public transit schedules, public events, varied attraction categories, and user personas for enhanced personalization. |
| Outcome: | The proposed dataset significantly improves meal scheduling, improving performance from 61% to 80% in the 7-day scenario. |
On the Compositionality Prediction of Noun Phrases using Poincaré Embeddings (P19-1)
Copied to clipboard
| Challenge: | idiomatic phrases have a non-compositional meaning, meanings of which can be derived from constituents and their grammatical relations. |
| Approach: | They propose to combine hierarchical and distributional information to blend hierarchic and distribution-based hierarchies to detect compositionality for noun phrases. |
| Outcome: | The proposed technique achieves significant improvements over state-of-the-art models based on distributional information and a weighted average of the distributional similarity and p-like function. |
WikiRef: Wikilinks as a route to recommending appropriate references for scientific Wikipedia pages (C18-1)
Copied to clipboard
| Challenge: | Existing methods to enhance Wikipedia's reference section are not effective. |
| Approach: | They propose a two-step approach that leverages the wikilinks present in a scientific Wikipedia target page and recommends highly relevant references to be included in that target page appropriately and automatically borrowed from the reference section of the wikipedia links. |
| Outcome: | The proposed approach achieves a notably good performance on two datasets from Computer Science and Physics. |
Text Takes Over: A Study of Modality Bias in Multimodal Intent Detection (2025.emnlp-main)
Copied to clipboard
| Challenge: | a new study examines the effectiveness of large language models and non-LLMs in multimodal intent detection . large-scale multimodal data integrations include text, audio, and visual inputs . |
| Approach: | They propose a framework to debias multimodal intent detection datasets by using human evaluation. |
| Outcome: | The proposed framework debiases the datasets and shows that mistral-7B outperforms most competitive models by approximately 9% on MIntRec-1 and 4% on MIndRec2.0. |
On Zero-Shot Counterspeech Generation by LLMs (2024.lrec-main)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) are used in numerous NLP tasks, including counterspeech generation. |
| Approach: | They propose three different prompting strategies for generating different types of counterspeech and propose a set of prompting techniques for counterspeak generation. |
| Outcome: | The proposed prompting strategies improve the performance of the models for counterspeech generation in two datasets, but with high toxicity with increase in model size. |
Incorporating Domain Knowledge into Medical NLI using Knowledge Graphs (D19-1)
Copied to clipboard
| Challenge: | Recent studies have shown that structured domain knowledge can be used for textual inference tasks in the medical domain. |
| Approach: | They propose to integrate structured domain knowledge into a knowledge graph for the Medical NLI task. |
| Outcome: | The proposed approach improves the baseline BioELMo architecture for the Medical NLI task. |
Using Distributional Thesaurus Embedding for Co-hyponymy Detection (2020.lrec-1)
Copied to clipboard
| Challenge: | Existing methods to detect lexical relations among distributionally similar words have been proposed to solve this problem. |
| Approach: | They propose to use distributional semantic models to detect co-hyponymy relations by embedding them into the distributional thesaurus. |
| Outcome: | The proposed model outperforms the state-of-the-art models for binary classification of co-hyponymy vs. hypernymy, as well as co-meronymy by huge margins. |
This is not a Disimprovement: Improving Negation Reasoning in Large Language Models via Prompt Engineering (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Negation reasoning remains a challenge for large language models (LLMs) a negative token attention score (NTAS) is introduced to quantify attention to negation words. |
| Approach: | They propose two genres of prompts that improve negation accuracy by up to 3.17% . they also propose a negative token attention score to quantify attention to negation words . |
| Outcome: | The proposed prompts improve negation accuracy and absolute accuracy by 3.17% over baselines. |
LexGLUE: A Benchmark Dataset for Legal Language Understanding in English (2022.acl-long)
Copied to clipboard
Ilias Chalkidis, Abhik Jana, Dirk Hartung, Michael Bommarito, Ion Androutsopoulos, Daniel Katz, Nikolaos Aletras
| Challenge: | Laws and their interpretations, legal arguments and agreements are typically expressed in writing. |
| Approach: | They propose a benchmark to evaluate model performance across legal NLU tasks . they also evaluate several generic and legal-oriented models . |
| Outcome: | The proposed model performs better across multiple tasks than previous models. |