Papers by Nebojsa Jojic
Investigating Agency of LLMs in Human-AI Collaboration Tasks (2024.eacl-long)
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| Challenge: | We examine how LLMs can be measured and managed for Agency . a model that manifests high Intentionality, Motivation, Self-Efficacy, and Self-Regulation is more likely to be perceived as strongly agentive. |
| Approach: | They collect a dataset of 83 human-human collaborative interior design conversations containing 908 conversational snippets annotated for Agency features. |
| Outcome: | The proposed models show that they manifest high Intentionality, Motivation, Self-Efficacy, and Self-Regulation, and are more likely to be perceived as agentive. |
Studying word order through iterative shuffling (2021.emnlp-main)
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| Challenge: | Recent work on large language models has made this hypothesis popular . but, word order is not important enough to make sentence structure relevant . |
| Approach: | They propose an efficient procedure that finds word order having highest likelihood under a fixed language model. |
| Outcome: | The proposed procedure can be used to find the ordering of a bag of words having the highest likelihood under a fixed language model. |
fLSA: Learning Semantic Structures in Document Collections Using Foundation Models (2025.emnlp-main)
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| Challenge: | Large language models (LLMs) can be used to solve new tasks by inducing high-level strategies from example solutions to similar problems and adapting these strategies to solve unseen problems. |
| Approach: | They propose a foundation-model-based Latent Semantic Analysis method that iteratively clusters and tags document segments based on document-level contexts. |
| Outcome: | The proposed method is more informative in reconstructing the original texts than existing tagging methods. |
Can MLLMs Find Their Way in a City? Exploring Emergent Navigation from Web-Scale Knowledge (2026.eacl-long)
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| Challenge: | Existing evaluation benchmarks for multimodal large language models (MLLMs) are language-centric or heavily reliant on simulated environments, rarely probing the nuanced, knowledge-intensive reasoning essential for practical, real-world scenarios. |
| Approach: | They propose a task of Sparsely Grounded Visual Navigation to evaluate MLLM-driven agents in city navigation in four diverse global cities. |
| Outcome: | The proposed benchmark encompassing four diverse global cities evaluates agents' decision-making abilities in city navigation. |
A Spatial Model for Extracting and Visualizing Latent Discourse Structure in Text (P18-1)
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| Challenge: | Using sequences of sentences, we show that learning long-range latent discourse structure from large corpora can be useful for machine learning. |
| Approach: | They propose a probabilistic model of documents as sequences of sentences with a 2- or 3-D spatial grid and embed sentences into a grid. |
| Outcome: | The proposed model outperforms or is competitive with state-of-the-art generative approaches on tasks such as predicting the outcome of a story, and sentence ordering. |
ThinkSum: Probabilistic reasoning over sets using large language models (2023.acl-long)
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| Challenge: | Large language models (LLMs) have a substantial capacity for high-level analogical reasoning, but they fail in scenarios that require reasoning over multiple objects or facts and making sequences of logical deductions. |
| Approach: | They propose a two-stage probabilistic inference paradigm, ThinkSum, which reasons over sets of objects or facts in a structured manner. |
| Outcome: | The proposed paradigm improves on the BIG-bench suite of evaluation tasks. |
Learning Web-based Procedures by Reasoning over Explanations and Demonstrations in Context (2020.acl-main)
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| Challenge: | a new direction for semantic parsing that models explanations to demonstrations is proposed . bottom-up approach to generating logical forms is complicated in domains with rich composition . |
| Approach: | They propose a new direction for semantic parsing that models explanations in a context . they use inverse semantics to reason backwards from observed demonstrations . |
| Outcome: | The proposed approach shows better task completion rates than a baseline method . it is competitive with exploration-and-demonstration based methods, but requires no exploration of environment . |
GPT Perdetry Test: Generating new meanings for new words (2021.naacl-main)
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| Challenge: | We create a set of nonce words and prompt GPT-3 to generate their dictionary definitions. |
| Approach: | They create a set of nonce words and prompt GPT-3 to generate their dictionary definitions. |
| Outcome: | The proposed model can process new words and make them 'neologisms' . it can also adapt to and extend a changing vocabulary, the authors found . |
Coherence boosting: When your pretrained language model is not paying enough attention (2022.acl-long)
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| Challenge: | Long-range semantic coherence remains a challenge in automatic language generation and understanding. |
| Approach: | They propose a procedure that increases a model’s focus on a long context by distributional analyses of generated ordinary text and dialog responses. |
| Outcome: | The proposed procedure increases the model's focus on a long context. |