instance_id int64 1 31 | paper_name stringlengths 10 101 | folder_name stringlengths 5 26 | paper_url stringlengths 32 50 | year int64 2.02k 2.03k | repo_url stringlengths 30 90 | repo_folder_name stringlengths 3 31 | implementations listlengths 1 5 |
|---|---|---|---|---|---|---|---|
11 | MoverScore: Text Generation Evaluating with Contextualized Embeddings and Earth Mover Distance | 11-moverscore | https://aclanthology.org/D19-1053.pdf | 2,019 | https://github.com/AIPHES/emnlp19-moverscore | moverscore | [
{
"category": "Evaluation Metrics & Benchmarking",
"class_name": "",
"goal_file": "movescore.py",
"goal_function": "word_mover_score",
"golden_file": "golden_files/movescore_golden.py",
"index": 1,
"instruction": "You need to implement the word_mover_score function in movescore.py based ... |
13 | Probing the Decision Boundaries of In-context Learning in Large Language Models | 13-DecBound | https://arxiv.org/pdf/2406.11233 | 2,024 | https://github.com/siyan-zhao/ICL_decision_boundary | DecBound-main | [
{
"category": "Data Augmentation & Generation",
"class_name": "",
"goal_file": "data_utils.py",
"goal_function": "generate_tasks",
"golden_file": "golden_files/data_utils_golden.py",
"index": 1,
"instruction": "Implement `generate_tasks` function in data_utils.py based on paper.pdf and t... |
14 | EmojiPrompt: Generative Prompt Obfuscation for Privacy-Preserving Communication with Cloud-based LLMs | 14-EmojiCrypt | https://arxiv.org/pdf/2402.05868 | 2,025 | https://github.com/agiresearch/EmojiCrypt | EmojiCrypt-main | [
{
"category": "Evaluation Metrics",
"class_name": "",
"goal_file": "Tabular.py",
"goal_function": "compute_mean_cosine_similarity",
"golden_file": "golden_files/Tabular_golden.py",
"index": 1,
"instruction": "Implement `compute_mean_cosine_similarity` function in Tabular.py based on the ... |
15 | Language Models Predict Empathy Gaps Between Social In-groups and Out-groups | 15-EmpathyBias | https://arxiv.org/pdf/2503.01030 | 2,025 | https://github.com/houyu0930/intergroup-empathy-bias | EmpathyBias-main | [
{
"category": "Evaluation Metrics",
"class_name": "",
"goal_file": "analysis/analysis_after_processing.py",
"goal_function": "get_delta",
"golden_file": "golden_files/analysis_after_processing_golden.py",
"index": 1,
"instruction": "Implement `get_delta` function analysis/analysis_after_... |
16 | Probing Language Models on Their Knowledge Source | 16-KnowProb | https://aclanthology.org/2024.blackboxnlp-1.35.pdf | 2,024 | https://github.com/Zineddine-Tighidet/knowledge-probing-framework | KnowProb-main | [
{
"category": "Interpretability & Explainability",
"class_name": "",
"goal_file": "src/classification/classifier.py",
"goal_function": "perform_classification_by_relation_group",
"golden_file": "golden_files/classifier_golden.py",
"index": 1,
"instruction": "Implement `perform_classifica... |
18 | Massive Activations in Large Language Models | 18-MassActiv | https://arxiv.org/abs/2402.17762 | 2,024 | https://github.com/locuslab/massive-activations | MassActiv-main | [
{
"category": "Interpretability & Explainability",
"class_name": "",
"goal_file": "main_vit.py",
"goal_function": "run_exp1",
"golden_file": "golden_files/main_vit_golden.py",
"index": 1,
"instruction": "Implement run_exp1 function in main_vit.py. Run 3D feature visualization for a speci... |
19 | Native Sparse Attention: Hardware-Aligned and Natively Trainable Sparse Attention | 19-Native-Sparse-Attention | https://arxiv.org/abs/2502.11089 | 2,025 | https://github.com/fla-org/native-sparse-attention | NSA | [
{
"category": "Neural Network Architectures & Modules",
"class_name": "",
"goal_file": "NSA.py",
"goal_function": "nsa",
"golden_file": "golden_files/NSA_golden.py",
"index": 1,
"instruction": "Please implement the `nsa` function in NSA.py based on the Natively trainable Sparse Attention... |
20 | Trusting Your Evidence: Hallucinate Less with Context-aware Decoding | 20-CAD | https://arxiv.org/abs/2305.14739 | 2,023 | https://github.com/xhan77/context-aware-decoding.git | context-aware-decoding-main | [
{
"category": "Decoding & Search Strategies",
"class_name": "",
"goal_file": "unit_test/unit_test_1.py",
"goal_function": "context_aware_sampling",
"golden_file": "golden_files/unit_test_1_golden.py",
"index": 1,
"instruction": "Implement the function context_aware_sampling in the unit_t... |
22 | Token-level Direct Preference Optimization | 22-TokenDPO | https://arxiv.org/abs/2404.11999 | 2,024 | https://github.com/Vance0124/Token-level-Direct-Preference-Optimization/tree/master | TokenDPO-main | [
{
"category": "Training Objectives & Optimization Techniques",
"class_name": "",
"goal_file": "trainers.py",
"goal_function": "tdpo_loss",
"golden_file": "golden_files/trainers_golden.py",
"index": 1,
"instruction": "Implement the `tdpo_loss` function in trainers.py based on the Token-le... |
23 | A Simple Framework for Contrastive Learning of Visual Representations | 23-SimCLR | https://arxiv.org/pdf/2002.05709 | 2,020 | https://github.com/skywalkerzhang/SimCLR/tree/master | SimCLR-main | [
{
"category": "Training Objectives & Optimization Techniques",
"class_name": "SimCLR",
"goal_file": "simclr.py",
"goal_function": "info_nce_loss",
"golden_file": "golden_files/simclr_golden.py",
"index": 1,
"instruction": "Implement the `info_nce_loss` method of `SimCLR` class in simclr.... |
25 | AdaLoRA: Adaptive Budget Allocation for Parameter-Efficient Fine-Tuning | 25-AdaLora | https://arxiv.org/pdf/2303.10512 | 2,023 | https://github.com/QingruZhang/AdaLoRA/tree/main | AdaLoRA | [
{
"category": "Neural Network Architectures & Modules",
"class_name": "SVDLinear",
"goal_file": "loralib/loralib/adalora.py",
"goal_function": "forward",
"golden_file": "golden_files/adalora_golden.py",
"index": 1,
"instruction": "Implement the forward function of SVD-based Adaptated lin... |
28 | Self-Play Fine-Tuning Converts Weak Language Models to Strong Language Models | 28-SPIN | https://arxiv.org/abs/2401.01335 | 2,024 | https://github.com/uclaml/SPIN | SPIN | [
{
"category": "loss function",
"class_name": "SPINTrainer",
"goal_file": "spin/alignment/trainer.py",
"goal_function": "spin_loss",
"golden_file": "golden_files/trainer_golden.py",
"index": 1,
"instruction": "Implement the `spin_loss` method of `SPINTrainer` class in `.spin/alignment/tra... |
30 | ERGO: Event Relational Graph Transformer for Document-level Event Causality Identification | 30-ERGO | https://aclanthology.org/2022.coling-1.185.pdf | 2,022 | https://github.com/chenmeiqii/ERGO | ERGO | [
{
"category": "Training Objectives & Optimization Techniques",
"class_name": "focal_loss",
"goal_file": "model.py",
"goal_function": "forward",
"golden_file": "golden_files/model_golden.py",
"index": 1,
"instruction": "Implement the loss function in the focal_loss class in model.py based... |
31 | Aspect-Based Sentiment Analysis with Syntax-Opinion-Sentiment Reasoning Chain | 31-Syn-Chain | https://aclanthology.org/2025.coling-main.210.pdf | 2,025 | https://github.com/rf-x/Syn-Chain-ABSA | Syn-Chain-ABSA | [
{
"category": "",
"class_name": "",
"goal_file": "conll_tree.py",
"goal_function": "spacy_result_to_conll",
"golden_file": "golden_files/conll_tree_golden.py",
"index": 1,
"instruction": "Implement the spacy_result_to_conll function in conll_tree.py to generate a dependency parse in the ... |
1 | Direct Preference Optimization: Your Language Model is Secretly a Reward Model | 01-DPO | https://arxiv.org/pdf/2305.18290 | 2,023 | https://github.com/eric-mitchell/direct-preference-optimization | direct-preference-optimization | [
{
"category": "Training Objectives & Optimization Techniques",
"class_name": "",
"goal_file": "trainers.py",
"goal_function": "preference_loss",
"golden_file": "golden_files/trainers_golden.py",
"index": 1,
"instruction": "Implement the preference_loss function in trainers.py based on th... |
2 | Language Models as Hierarchy Encoders | 02-HITs | https://arxiv.org/pdf/2401.11374 | 2,024 | https://github.com/KRR-Oxford/HierarchyTransformers | HierarchyTransformers | [
{
"category": "Training Objectives & Optimization Techniques",
"class_name": "HierarchyTransformerLoss",
"goal_file": "src/hierarchy_transformers/losses/hit_loss.py",
"goal_function": "forward",
"golden_file": "golden_files/hit_loss_golden.py",
"index": 1,
"instruction": "Implement the f... |
3 | DoLa: Decoding by Contrasting Layers Improves Factuality in Large Language Models | 03-DoLa | https://arxiv.org/abs/2309.03883 | 2,023 | https://github.com/voidism/DoLa.git | DoLa-main | [
{
"category": "Decoding & Search Strategies",
"class_name": "",
"goal_file": "unit_test/unit_test_1.py",
"goal_function": "dola_greedy_decode_agent",
"golden_file": "golden_files/unit_test_1_golden.py",
"index": 1,
"instruction": "Implement the function dola_greedy_decode_agent in the un... |
4 | Exploring Concept Depth: How Large Language Models Acquire Knowledge and Concept at Different Layers? | 04-CD | https://arxiv.org/abs/2404.07066 | 2,024 | https://github.com/Luckfort/CD | CD-main | [
{
"category": "Interpretability & Explainability",
"class_name": "",
"goal_file": "main.py",
"goal_function": "probing",
"golden_file": "golden_files/main_golden.py",
"index": 1,
"instruction": "Implement `probing` function in main.py based on paerp.pdf and the repository. Generate Probi... |
5 | SimPO: Simple Preference Optimization with a Reference-Free Reward | 05-SimPO | https://arxiv.org/abs/2405.14734 | 2,024 | https://github.com/princeton-nlp/SimPO | SimPO | [
{
"category": "loss function",
"class_name": "SimPOTrainer",
"goal_file": "scripts/simpo_trainer.py",
"goal_function": "simpo_loss",
"golden_file": "golden_files/simpo_trainer_golden.py",
"index": 1,
"instruction": "Implement the `simpo_loss` function of `SimPOTrainer` class in './script... |
6 | Representation Engineering: A Top-Down Approach to AI Transparency | 06-RepE | https://arxiv.org/abs/2310.01405 | 2,023 | https://github.com/andyzoujm/representation-engineering | representation-engineering-main | [
{
"category": "Feature Learning & Representation",
"class_name": "",
"goal_file": "unit_test/unit_test_1.py",
"goal_function": "get_rep_directions_agent",
"golden_file": "golden_files/unit_test_1_golden.py",
"index": 1,
"instruction": "Implement the function get_rep_directions_agent in t... |
7 | BERTScore: Evaluating Text Generation with BERT | 07-bertscore | https://openreview.net/pdf?id=SkeHuCVFDr | 2,020 | https://github.com/Tiiiger/bert_score/tree/master | bert_score | [
{
"category": "Evaluation Metrics",
"class_name": "",
"goal_file": "bert_score_goal.py",
"goal_function": "greedy_cos_idf",
"golden_file": "golden_files/bert_score_golden.py",
"index": 1,
"instruction": "You need to implement the greedy_cos_idf function in bert_score_goal.py based on pap... |
8 | StepLength | 08-StepLength | https://arxiv.org/abs/2401.04925 | 2,024 | https://github.com/MingyuJ666/The-Impact-of-Reasoning-Step-Length-on-Large-Language-Models | stepLength-main | [
{
"category": "Prompt Engineering & Instruction Tuning",
"class_name": "",
"goal_file": "run_inference.py",
"goal_function": "get_sentence",
"golden_file": "golden_files/run_inference_golden.py",
"index": 1,
"instruction": "Implement `get_sentence` function in run_inference.py based on p... |
9 | Prompt-Based Monte-Carlo Tree Search for Goal-oriented Dialogue Policy Planning | 09-GDPZero | https://aclanthology.org/2023.emnlp-main.439.pdf | 2,023 | https://github.com/jasonyux/GDPZero | GDPZero | [
{
"category": "Decoding & Search Strategies",
"class_name": "OpenLoopMCTS",
"goal_file": "core/mcts.py",
"goal_function": "find_best_action",
"golden_file": "golden_files/mcts_golden.py",
"index": 1,
"instruction": "Implement the self.find_best_action function in the OpenLoopMCTS class i... |
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