Papers by Danish Contractor
Prompting with Pseudo-Code Instructions (2023.emnlp-main)
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| Challenge: | ambiguity in natural language can hinder performance of large language models. |
| Approach: | They manually create a dataset of pseudo-code prompts for 132 different classification, QA, and generative language tasks, sourced from the Super-NaturalInstructions dataset. |
| Outcome: | The pseudo-code prompts improve the performance of two LLM families, BLOOM and CodeGen. |
Exemplar Encoder-Decoder for Neural Conversation Generation (P18-1)
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| Challenge: | Existing approaches to generate conversational systems suffer from lack of diversity in responses and generation of short, repetitive and uninteresting responses. |
| Approach: | They propose a novel conversation model that uses similar examples from training data to generate responses. |
| Outcome: | The proposed model outperforms state-of-the-art sequence to sequence learning on several evaluation metrics on two large data sets. |
Neural Conversational QA: Learning to Reason vs Exploiting Patterns (2020.emnlp-main)
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| Challenge: | Neural Conversational QA tasks such as ShARC require systems to answer questions based on the contents of a given passage. |
| Approach: | They propose to modify a data-set with fewer spurious patterns to exploit them . they also propose to build a heuristic-based program to exploit spurious clues . |
| Outcome: | The proposed program exploits spurious patterns in the ShARC dataset, compared to neural models. |
Agent Assist through Conversation Analysis (2020.emnlp-demos)
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Kshitij Fadnis, Nathaniel Mills, Jatin Ganhotra, Haggai Roitman, Gaurav Pandey, Doron Cohen, Yosi Mass, Shai Erera, Chulaka Gunasekara, Danish Contractor, Siva Patel, Q. Vera Liao, Sachindra Joshi, Luis Lastras, David Konopnicki
| Challenge: | Using conversational approach to information retrieval for agent assistance, customer support agents are a critical part of an organization's customer support team. |
| Approach: | They propose a conversational approach to information retrieval for agent assistance that monitors an evolving conversation and recommends both responses and URLs of documents. |
| Outcome: | The proposed system monitors an evolving conversation and recommends both responses and URLs of documents the agent can use in replies to their client. |
Multi-Level Memory for Task Oriented Dialogs (N19-1)
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| Challenge: | Recent task oriented dialog systems use memory architectures to incorporate external knowledge in their dialogs. |
| Approach: | They propose a novel multi-level memory architecture that separates dialog context and knowledge base results . they use cells for each query and their corresponding results to address queries . |
| Outcome: | The proposed architecture outperforms current state-of-the-art models on three publicly available data sets. |
Joint Reasoning on Hybrid-knowledge sources for Task-Oriented Dialog (2023.findings-eacl)
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| Challenge: | Existing systems for task oriented dialog use knowledge present only in structured knowledge sources to generate responses. |
| Approach: | They propose a model that assumes that information is always present in a structured knowledge base . they also refine the model to take into account the fact that it can fuse information from structured and unstructured knowledge sources. |
| Outcome: | The proposed model is robust to perturbations to knowledge modality and can fuse information from structured and unstructured knowledge to generate responses. |
Simulated Chats for Building Dialog Systems: Learning to Generate Conversations from Instructions (2021.findings-emnlp)
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| Challenge: | Popular dialog datasets such as MultiWOZ are created by providing crowd workers with instructions that describe the task to be accomplished. |
| Approach: | They propose a data creation strategy that uses a pre-trained language model to simulate the interaction between crowd workers by creating a user bot and an agent bot. |
| Outcome: | The proposed data creation strategy improves on two publicly available datasets using a pre-trained language model and a smaller percentage of actual crowd-generated conversations and their corresponding instructions. |
Mix-and-Match: Scalable Dialog Response Retrieval using Gaussian Mixture Embeddings (2022.findings-emnlp)
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| Challenge: | Existing approaches for dialog response retrieval embed the context-response pairs as points in the embedding space. |
| Approach: | They propose a scalable model that can learn complex relationships between context-response pairs . they train the models by optimizing the Kullback-Leibler divergence between the distributions induced by context-responders in the training data. |
| Outcome: | The proposed model performs better than other embedding-based approaches on public conversation data. |
Live API-Bench: 2500+ Live APIs for Testing Multi-Step Tool Calling (2026.eacl-long)
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Benjamin Elder, Anupama Murthi, Jungkoo Kang, Ankita Rajaram Naik, Kiran Kate, Kinjal Basu, Danish Contractor
| Challenge: | Large language models rely on external tools and APIs to perform tasks specified in natural language. |
| Approach: | They propose a benchmark that transforms SQL queries from BIRD-SQL into executable API sequences. |
| Outcome: | The proposed benchmark evaluates 10 LLMs and 4 ReACT agents with low task completion rates and 50% task completion rate. |
Spotlight Your Instructions: Instruction-following with Dynamic Attention Steering (2026.eacl-long)
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| Challenge: | In many real-world applications, users rely on natural language instructions to guide large language models (LLMs) However, LLMs do not attend to these instructions reliably, and users lack simple mechanisms to emphasize their importance beyond modifying prompt wording or structure. |
| Approach: | They propose an inference-time method that enables users to emphasize specific parts of their prompt by steering the model’s attention toward them, aligning the model's perceived importance of different tokens with user intent. |
| Outcome: | The proposed method improves instruction following across tasks involving multiple instructions and generalizes across models of varying scales. |
Semi-Structured Object Sequence Encoders (2023.findings-emnlp)
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Rudra Murthy, Riyaz Bhat, Chulaka Gunasekara, Siva Patel, Hui Wan, Tejas Dhamecha, Danish Contractor, Marina Danilevsky
| Challenge: | Semi-structured object sequences are often represented as a sequence of key-value pairs over time . authors propose a two-part approach that takes each key independently and encodes a representation of its values over time. |
| Approach: | They propose a two-part approach that first considers each key independently and encodes a representation of its values over time. |
| Outcome: | The proposed approach outperforms existing methods on multiple prediction tasks using real-world data. |