Papers with learner
Learning to request guidance in emergent language (D19-64)
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Benjamin Kolb, Leon Lang, Henning Bartsch, Arwin Gansekoele, Raymond Koopmanschap, Leonardo Romor, David Speck, Mathijs Mul, Elia Bruni
| Challenge: | Previous research into agent communication has shown that a pre-trained guide can speed up the learning process of an imitation learning agent. |
| Approach: | They extend one-directional communication by a one-bit communication channel from the learner back to the guide and limit the guidance by penalizing the learners for these requests. |
| Outcome: | The proposed guide can speed up the learning process of an imitation learning agent by providing the learner with discrete messages in an emerged language about how to solve the task. |
Automatic Gloss Dictionary for Sign Language Learners (2022.acl-demo)
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| Challenge: | 430 million people worldwide have developed hearing loss and 700 million more are learning a sign language as a second language . sign language learners have limited means of seeking assistance and are restricted to class offerings or relying on a webcam to look up the sign. |
| Approach: | They propose an online tool supporting 2, 000 signs to assist language learners in determining the meaning of given signs. |
| Outcome: | The proposed system can lower the barrier in sign language learning by addressing the common problem of sign finding and make it accessible to the wider community. |
IntelliCode: A Multi-Agent LLM Tutoring System with Centralized Learner Modeling (2026.eacl-demo)
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| Challenge: | Existing LLM tutors lack persistent representations of learner knowledge . current systems provide inconsistent hints, overlook dependencies between concepts . |
| Approach: | They propose a multi-agent LLM tutoring system that integrates mastery estimates, misconceptions, review schedules, and engagement signals. |
| Outcome: | The proposed system integrates skill assessment, learner profiling, graduated hinting, curriculum selection, spaced repetition, and engagement monitoring over a shared state under a single-writer policy. |
ReAL: How Can LLMs Simulate the Real Teacher? Retrieval-enhanced Agent for Adaptive Learning (2025.findings-emnlp)
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| Challenge: | Prior methods model learner-item interactions based only on ID sequences, leading to insufficient use of both learner and item information. |
| Approach: | They propose a Retrieval-enhanced Agent for Adaptive Learning powered by large language models to simulate teacher decision-making with extensive prior knowledge and teaching experience. |
| Outcome: | The proposed model outperforms existing models on three real-world datasets in both internal and external perspectives. |
Controlling Grammatical Error Correction Using Word Edit Rate (P19-2)
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| Challenge: | Existing models for grammatical error correction only consider the single degree of correction suited for training corpus. |
| Approach: | They propose a neural grammar error correction method that can control the degree of correction by using new training data annotated with word edit rate. |
| Outcome: | The proposed method improves correction accuracy by using training data annotated with word edit rate. |
Being Negative but Constructively: Lessons Learnt from Creating Better Visual Question Answering Datasets (N18-1)
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| Challenge: | Visual question answering datasets are a form of (visual) Turing test that artificial intelligence should strive to achieve. |
| Approach: | They propose automatic procedures to remedy design deficiencies in visual question answering datasets . they propose to use a set of decoys to re-construct decoying answers for two popular Visual QA datasets. |
| Outcome: | The proposed procedures improve the performance of the proposed datasets. |
SW4ALL: a CEFR Classified and Aligned Corpus for Language Learning (L18-1)
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| Challenge: | Learning a second language requires exposition to texts, especially for the acquisition of vocabulary. |
| Approach: | They propose a corpus of documents classified by language proficiency level . they use alignments between the English Wikipedia and the Simple English Wikipedia . |
| Outcome: | The SW4ALL corpus contains 8,669 pairs of documents that present different levels of proficiency. |
Sibylvariant Transformations for Robust Text Classification (2022.findings-acl)
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| Challenge: | Existing text transformation techniques are limited in their ability to expand input space . many techniques can artificially expand labeled training sets or test suites, but are class-preserving . |
| Approach: | They propose a concept of sibylvariance to describe transforms that relax the label-preserving constraint and knowably vary the expected class. |
| Outcome: | The proposed transforms can expand input space, but they are limited in their ability to expand . the proposed transform can knowably vary the expected class and lead to more diverse distributions . |
MetaSLRCL: A Self-Adaptive Learning Rate and Curriculum Learning Based Framework for Few-Shot Text Classification (2022.coling-1)
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| Challenge: | Existing few-shot text classification methods lack labeled data in many scenarios. |
| Approach: | They propose a meta learning framework that obtains different learning rates for different tasks and neural network layers to enable the meta learner to quickly adapt to new training data. |
| Outcome: | The proposed framework can obtain different learning rates for different tasks and neural network layers so as to enable the meta learner to quickly adapt to new tasks. |
Pre-Learning Environment Representations for Data-Efficient Neural Instruction Following (P19-1)
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| Challenge: | Using logical forms, neural networks can sometimes require orders of magnitude more data to map from natural language instructions to state transitions (actions) |
| Approach: | They propose to map from natural language instructions to state transitions (actions) they augment a baseline learner with an initial environment-learning phase that uses observations of language-free state transition to induce a suitable latent representation of actions before processing the instruction-following training data. |
| Outcome: | The proposed model improves performance over systems whose representations are learned from limited instructional data alone. |
Automatic Distractor Generation for Multiple Choice Questions in Standard Tests (2020.coling-main)
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| Challenge: | Existing methods to generate distractors for multiple choice questions are expensive and time-consuming. |
| Approach: | They propose a question and answer guided distractor generation framework to automate distractors generated by domain experts. |
| Outcome: | The proposed model outperforms existing models and achieves state-of-the-art on a large-scale dataset. |
Attending via both Fine-tuning and Compressing (2021.findings-acl)
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| Challenge: | Existing studies show that attention mechanisms can improve models' interpretation, but they are not explicable. |
| Approach: | They propose a framework consisting of a learner and a compressor to purify attention scores . they propose to fine-tune and compress the attention mechanism to obtain a more faithful explanation . |
| Outcome: | The proposed framework improves performance and interpretability on eight benchmark datasets. |
Negative language transfer in learner English: A new dataset (2021.naacl-main)
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| Challenge: | This dataset contains annotated error causes for learner writing errors that tie learner mistakes to structures from their first language. |
| Approach: | They propose a learner English dataset enhanced with annotated error causes and concrete examples of learner errors that relate to their first languages. |
| Outcome: | The proposed dataset will be used to analyze learner errors related to language transfer from the learners’ first language. |
Assessing the Helpfulness of Learning Materials with Inference-Based Learner-Like Agent (2020.emnlp-main)
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| Challenge: | Prior work uses hand-crafted scores to recommend sentences but has difficulty adopting such scores to all the near-synonyms as near-near-sonyms differ in various ways. |
| Approach: | They propose an inference-based learner-like agent to mimic learner behavior and identify good learning materials by examining the agent's performance. |
| Outcome: | The proposed agent achieves the best performance in fill-in-the-blank and good example sentence selection tasks. |
Investigating the Zone of Proximal Development of Language Models for In-Context Learning (2025.findings-naacl)
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| Challenge: | In-context learning is a dynamic and progressive process where learners integrate new information into their knowledge base through interactions with the environment. |
| Approach: | They propose a learning analytics framework to analyze the in-context learning behavior of large language models (LLMs) through the lens of the Zone of Proximal Development (ZPD), an established theory in educational psychology. |
| Outcome: | The proposed framework improves inference and fine-tuning scenarios by selectively applying it to queries that are most likely to benefit from demonstrations. |
AiRO - an Interactive Learning Tool for Children at Risk of Dyslexia (2022.lrec-1)
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| Challenge: | AiRO learning tool is designed for use in classrooms and homes by children at risk of developing dyslexia. |
| Approach: | They propose to use the AiRO learning tool in classrooms and homes by children at risk of developing dyslexia. |
| Outcome: | The AiRO learning tool outperforms the control group in the first test 'in vivo' with 49 pupils aged 6 . |
Learning from Evolving Training Dynamics: An Entropy-Maximizing Data Curation Strategy for LLM Supervised Post-Training (2026.acl-long)
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| Challenge: | EVO-Curate is a dynamic data curation framework that synchronizes sample complexity with the maturing capacity of the Large Language Model (LLM). |
| Approach: | They propose a dynamic data curation framework that synchronizes sample complexity with maturing capacity of the Large Language Model (LLM) they use an Adaptive Dynamics Measurer to synthesize instantaneous difficulty and historical variability into a multidimensional utility score. |
| Outcome: | The proposed framework outperforms standard training baselines and traditional CL methods on instruction following, mathematical reasoning, and code generation architectures while maintaining manageable computational overhead. |
Measuring Inductive Biases of In-Context Learning with Underspecified Demonstrations (2023.acl-long)
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| Challenge: | In-context learning is an important paradigm for adapting large language models to new tasks . but the generalization behavior of ICL remains poorly understood . |
| Approach: | They characterize the feature biases of large language models by constructing underspecified demonstrations . they find that LLMs exhibit clear feature bias, and they evaluate interventions . |
| Outcome: | The proposed model prefers the "default" task features over distractor features more often than the base model. |
TYPIC: A Corpus of Template-Based Diagnostic Comments on Argumentation (2022.lrec-1)
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Shoichi Naito, Shintaro Sawada, Chihiro Nakagawa, Naoya Inoue, Kenshi Yamaguchi, Iori Shimizu, Farjana Sultana Mim, Keshav Singh, Kentaro Inui
| Challenge: | Argumentation and debate are effective tools for developing critical thinking skills, but it requires a lot of time and effort. |
| Approach: | They propose to automate the process of giving diagnostic comments to students . they define criteria for a template set that can be used to evaluate the model . |
| Outcome: | The proposed model can be used to evaluate arguments and evaluate them in real time. |
Transfer and Active Learning for Dissonance Detection: Addressing the Rare-Class Challenge (2023.acl-long)
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Vasudha Varadarajan, Swanie Juhng, Syeda Mahwish, Xiaoran Liu, Jonah Luby, Christian Luhmann, H. Andrew Schwartz
| Challenge: | Active learning has been proposed to alleviate data acquisition challenges for rare-class tasks when the class label is very infrequent (e.g., 5% of samples). |
| Approach: | They propose to use transformers to train models on closely related tasks and evaluate acquisition strategies, including a proposed probability-of-rare-class approach to dissonance detection. |
| Outcome: | The proposed method improves model accuracy while iterative transfer-learning does not improve cold-start performance. |
Grammatical Error Correction in Low Error Density Domains: A New Benchmark and Analyses (2020.emnlp-main)
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| Challenge: | CWEB is a new benchmark for grammatical error correction (GEC) systems . website data contains far fewer grammamatical errors than learner essays . |
| Approach: | They propose to broaden the target domain of grammatical error correction (GEC) systems . website data contains far fewer grammamatical errors than learner essays . |
| Outcome: | The proposed model can't rely on a strong internal language model in low error density domains. |
MalruleLib: Large-Scale Executable Misconception Reasoning with Step Traces for Modeling Student Thinking in Mathematics (2026.acl-long)
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| Challenge: | MalruleLib is a learning-science-grounded framework that translates documented misconceptions into executable procedures and generates step-by-step traces of malrule-consistent student reasoning. |
| Approach: | They propose a learning-science-grounded framework that translates documented misconceptions into executable procedures and generates step-by-step traces of malrule-consistent student reasoning. |
| Outcome: | The framework translates misconceptions into executable procedures and generates step-by-step traces of malrule-consistent student reasoning. |
Improving the robustness of NLI models with minimax training (2023.acl-long)
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| Challenge: | Experimental results show that our method consistently outperforms other robustness enhancement techniques on out-of-distribution adversarial test sets, while maintaining high in-distance accuracy. |
| Approach: | They propose a minimax objective between a learner model being trained for the task and an auxiliary model aiming to maximize the learner's loss by up-weighting underrepresented "hard" examples with patterns that contradict the shortcuts learned from the prevailing "easy" examples. |
| Outcome: | The proposed method outperforms other robustness enhancement techniques on out-of-distribution adversarial test sets while maintaining high in-distance accuracy. |
From Off-Policy to On-Policy: Enhancing GUI Agents via Bi-level Expert-to-Policy Assimilation (2026.acl-long)
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| Challenge: | Vision-language models are increasingly deployed as computer-use agents that operate desktops and browsers. |
| Approach: | They propose a method that turns static expert traces into policy-aligned guidance . they propose RLVR with a per-task, dynamically updated cache to decompose planning and execution . |
| Outcome: | The proposed model improves UITARS1.5-7B success from 22.87% to 32.13% on OSWorld-Verified and raises a held-out split from 5.74% to 10.30% on MMBench-GUI and Online-Mind2Web. |
PhoniTale: Phonologically Grounded Mnemonic Generation for Typologically Distant Language Pairs (2025.emnlp-main)
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| Challenge: | Vocabulary acquisition is a challenge for second-language learners when learning typologically distant languages such as English and Korean, where phonological and structural mismatches complicate vocabulary learning. |
| Approach: | They propose a cross-lingual mnemonic generation system that performs IPA-based phonological adaptation and syllable-aware alignment to retrieve L1 keyword sequence and uses LLMs to generate verbal cues. |
| Outcome: | The proposed system outperforms human-written and automated mnemonics in a short-term recall test with human participants and achieves quality comparable to human-writing mnms. |
ELI-Why: Evaluating the Pedagogical Utility of Language Model Explanations (2025.findings-acl)
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Brihi Joshi, Keyu He, Sahana Ramnath, Sadra Sabouri, Kaitlyn Zhou, Souti Chattopadhyay, Swabha Swayamdipta, Xiang Ren
| Challenge: | Language models are widely used in education, yet their ability to tailor responses to learners with varied informational needs and knowledge backgrounds remains under-explored. |
| Approach: | They conduct two extensive human studies to assess the utility of language model-generated explanatory answers (explanations) on a benchmark of 13.4K "Why" questions. |
| Outcome: | The proposed model explanations match learners' educational backgrounds only 50% of the time, compared to 79% for lay explanations. |