Papers with random baselines
Information-Theoretic Probing with Minimum Description Length (2020.emnlp-main)
Copied to clipboard
| Challenge: | Despite widespread adoption of probes, differences in their accuracy fail to adequately reflect differences in representations. |
| Approach: | They propose an alternative to the standard probes, information-theoretic probing with minimum description length (MDL). |
| Outcome: | The proposed method agrees in results and is more informative and stable than the standard probes. |
EmoNoBa: A Dataset for Analyzing Fine-Grained Emotions on Noisy Bangla Texts (2022.aacl-short)
Copied to clipboard
| Challenge: | EmoNoBa is a dataset for fine-grained emotion detection on Bangla text . it is based on 22698 comments from social media sites on 12 domains . |
| Approach: | They propose a manually annotated dataset of 22,698 Bangla comments from social media sites on 12 different domains to use for fine-grained emotion detection. |
| Outcome: | The proposed dataset of 22,698 public comments on 12 domains shows that hand-crafted features perform better than neural networks and pre-trained language models. |
Chasing Random: Instruction Selection Strategies Fail to Generalize (2025.findings-naacl)
Copied to clipboard
| Challenge: | Prior work has shown that language models can be tuned to follow user instructions using only a small set of high-quality instructions. |
| Approach: | They analyze popular selection strategies across different datasets and benchmarks to find out whether they generalize poorly. |
| Outcome: | The proposed methods outperform random baselines and cost-performance trade-offs on the full dataset and a random subset. |
Investigating Multi-source Active Learning for Natural Language Inference (2023.eacl-main)
Copied to clipboard
| Challenge: | Recent studies often assume that training and test data are drawn from the same distribution. |
| Approach: | They propose to apply active learning to unlabelled data pools to test for learning and generalisation. |
| Outcome: | The proposed strategies outperform random selection and outperformed hard-to-learn data on the task of natural language inference. |
Can You Tell Me How to Get Past Sesame Street? Sentence-Level Pretraining Beyond Language Modeling (P19-1)
Copied to clipboard
Alex Wang, Jan Hula, Patrick Xia, Raghavendra Pappagari, R. Thomas McCoy, Roma Patel, Najoung Kim, Ian Tenney, Yinghui Huang, Katherin Yu, Shuning Jin, Berlin Chen, Benjamin Van Durme, Edouard Grave, Ellie Pavlick, Samuel R. Bowman
| Challenge: | State-of-the-art models in natural language processing (NLP) often incorporate sentence encoder functions which generate a sequence of vectors intended to represent the in-context meaning of each word in an input text. |
| Approach: | They conduct the first large-scale systematic study of candidate pretraining tasks, comparing 19 different tasks as alternatives and complements to language modeling. |
| Outcome: | The proposed model can be used to train sentences on language modeling tasks. |
MILU: A Multi-task Indic Language Understanding Benchmark (2025.naacl-long)
Copied to clipboard
| Challenge: | Existing benchmarks focus on English, leaving substantial gaps in assessing LLM capabilities in low-resource and linguistically diverse languages. |
| Approach: | They propose a multi-task indic language understanding benchmark to assess LLMs in low-resource languages. |
| Outcome: | The new benchmark spans 8 domains and 41 subjects across 11 Indic languages, reflecting general and culturally specific knowledge. |
Beyond Facts- Benchmarking Distributional Reading Comprehension in Large Language Models (2026.findings-acl)
Copied to clipboard
Pei-Fu Guo, Ya An Tsai, Chun-Chia Hsu, Kai-Xin Chen, Yun-Da Tsai, Kai-Wei Chang, Nanyun Peng, Mi-Yen Yeh, Shou-De Lin
| Challenge: | Existing reading comprehension benchmarks focus on factual information, but many real-world tasks require distributional knowledge expressed across text. |
| Approach: | They propose a reading comprehension benchmark for LLMs to evaluate their ability to infer distributional knowledge from natural language. |
| Outcome: | Experiments with multiple LLMs show that the model outperforms baselines, but performance varies widely across distribution types and characteristics. |
PhageBench: Can LLMs Understand Raw Bacteriophage Genomes? (2026.findings-acl)
Copied to clipboard
| Challenge: | phage genome annotation is a critical component of microbial ecosystems and antibiotics. |
| Approach: | They propose a benchmark to evaluate phage genome understanding by mirroring workflow of bioinformatics experts. |
| Outcome: | The benchmark outperforms baseline models in phage contig identification and host prediction. |
GenomeQA: Benchmarking General Large Language Models for Genome Sequence Understanding (2026.acl-long)
Copied to clipboard
| Challenge: | Existing benchmarks focus on specialized DNA models trained for sequence prediction or evaluate biological knowledge using text-only questions. |
| Approach: | They propose a benchmark to evaluate general-purpose LLMs on sequence-based genome inference tasks. |
| Outcome: | The proposed benchmark outperforms baseline models on sequence-based genome inference tasks. |