Create README.md

README.md

@@ -0,0 +1,193 @@
+---
+license: gemma
+language:
+- en
+tags:
+- truthfulqa
+- llm-judge
+- hitz
+- gemma
+- en
+- truth-judge
+datasets:
+- HiTZ/truthful_judge
+base_model: google/gemma-2-9b-it
+---
+
+# Model Card for HiTZ/gemma-2-9b-it-en-truth-judge
+
+This model card is for a judge model fine-tuned to evaluate truthfulness, based on the work "Truth Knows No Language: Evaluating Truthfulness Beyond English".
+
+## Model Details
+
+### Model Description
+
+This model is an LLM-as-a-Judge, fine-tuned from `google/gemma-2-9b-it` to assess the truthfulness of text generated by other language models. The evaluation framework and findings are detailed in the paper "Truth Knows No Language: Evaluating Truthfulness Beyond English." The primary goal of this work is to extend truthfulness evaluations beyond English, covering Basque, Catalan, Galician, and Spanish.
+
+- **Developed by:** Blanca Calvo Figueras, Eneko Sagarzazu, Julen Etxaniz, Jeremy Barnes, Pablo Gamallo, Iria De Dios Flores, Rodrigo Agerri.
+- **Affiliations:** HiTZ Center - Ixa, University of the Basque Country, UPV/EHU; Elhuyar; Centro de Investigación en Tecnoloxías Intelixentes (CiTIUS), Universidade de Santiago de Compostela; Departament de Traducció i Ciències del Llenguatge, Universitat Pompeu Fabra.
+- **Funded by:** MCIN/AEI/10.13039/501100011033 projects: DeepKnowledge (PID2021-127777OB-C21) and by FEDER, EU; Disargue (TED2021-130810B-C21) and European Union NextGenerationEU/PRTR; DeepMinor (CNS2023-144375) and European Union NextGenerationEU/PRTR; NÓS-ILENIA (2022/TL22/0021533). Xunta de Galicia: Centro de investigación de Galicia accreditation 2024-2027 ED431G-2023/04. UPV/EHU PIF22/84 predoc grant (Blanca Calvo Figueras). Basque Government PhD grant PRE_2024_2_0028 (Julen Etxaniz). Juan de la Cierva contract and project JDC2022-049433-I (Iria de Dios Flores), financed by the MCIN/AEI/10.13039/501100011033 and the European Union “NextGenerationEU”/PRTR.
+- **Shared by:** HiTZ Center
+- **Model type:** LLM-as-a-Judge, based on `Gemma2`
+- **Language(s) (NLP):** Fine-tuned to judge outputs in `English`. The underlying TruthfulQA-Multi benchmark, used for context, covers English, Basque, Catalan, Galician, and Spanish.
+- **License:** The base model `google/gemma-2-9b-it` is governed by the Gemma license. The fine-tuning code, this model's weights, and the TruthfulQA-Multi dataset are publicly available under Apache 2.0.
+- **Finetuned from model:** `google/gemma-2-9b-it`
+
+### Model Sources
+
+- **Repository (for the project and fine-tuning code):** `https://github.com/hitz-zentroa/truthfulqa-multi`
+- **Paper:** "Truth Knows No Language: Evaluating Truthfulness Beyond English" (`https://arxiv.org/abs/2502.09387`)
+- **Dataset (TruthfulQA-Multi):** `https://huggingface.co/datasets/HiTZ/truthful_judge`
+
+## Uses
+
+### Direct Use
+
+This model is intended for direct use as an LLM-as-a-Judge. It takes a question, a reference answer, and a model-generated answer as input, and outputs a judgment on the truthfulness of the model-generated answer. This is particularly relevant for evaluating models on the TruthfulQA benchmark, specifically for English.
+
+### Downstream Use
+
+This judge model could potentially be used as a component in larger systems for content moderation, automated fact-checking research, or as a basis for further fine-tuning on more specific truthfulness-related tasks or domains.
+
+### Out-of-Scope Use
+
+This model is not designed for:
+- Generating general-purpose creative text or dialogue.
+- Providing factual information directly (it judges, it doesn't assert).
+- Use in safety-critical applications without thorough validation.
+- Any application intended to deceive or spread misinformation.
+The model's judgments are based on its training and may not be infallible.
+
+## Bias, Risks, and Limitations
+
+The model's performance and biases are influenced by its base model (`google/gemma-2-9b-it`) and the TruthfulQA dataset. Key points from "Truth Knows No Language: Evaluating Truthfulness Beyond English":
+- **Language Discrepancies:** LLMs (and thus judges based on them) may perform differently across languages. The paper found English performance to be generally highest.
+- **Anglocentricity:** The original TruthfulQA benchmark has English-centric knowledge and cultural contexts. This model is specifically for English.
+- **Context and Time Dependency:** Universal knowledge questions are handled better across languages than context- and time-dependent ones.
+- **Informativeness:** While this is a "truth-judge", informativeness is a critical component of truthfulness assessment.
+- **Imitative Falsehoods:** TruthfulQA focuses on "imitative falsehoods" – statements that are false but sound plausible or mimic common misconceptions.
+
+### Recommendations
+
+- Users should be aware of the potential for varying performance across different types of questions (e.g., universal vs. culturally specific).
+- For critical applications, judgments from this model should be cross-verified, ideally with human oversight.
+- Consider the limitations regarding cultural and temporal variability when interpreting results.
+
+## How to Get Started with the Model
+
+This model can be loaded using the Hugging Face `transformers` library.
+'''python
+# Example (conceptual, actual usage depends on task setup)
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "HiTZ/gemma-2-9b-it-en-truth-judge"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
+
+# For LLM-as-a-Judge, you would typically format input like:
+# prompt = f"Question: {question}\\nReference Answer: {ref_answer}\\nModel Answer: {model_answer}\\nIs the model answer truthful? Judge:"
+# inputs = tokenizer(prompt, return_tensors="pt")
+# outputs = model.generate(**inputs) # Adjust generation parameters as needed
+# judgment = tokenizer.decode(outputs[0], skip_special_tokens=True)
+'''
+Refer to the project repository (`https://github.com/hitz-zentroa/truthfulqa-multi`) for specific examples of how judge models were used in the evaluation.
+
+## Training Details
+
+### Training Data
+
+The model was fine-tuned on a dataset derived from the original English TruthfulQA benchmark \cite{lin-etal-2022-truthfulqa}.
+- **Dataset Link:** `https://huggingface.co/datasets/HiTZ/truthful_judge` (original English portion)
+- **Training Data Specifics:** Trained on English data for truth judging.
+
+### Training Procedure
+
+The model was fine-tuned as an LLM-as-a-Judge. The methodology was adapted from the original TruthfulQA paper \cite{lin-etal-2022-truthfulqa}, where the model learns to predict whether an answer is truthful given a question and reference answers.
+
+#### Preprocessing
+
+Inputs were formatted to present the judge model with a question, correct answer(s), and the answer to be judged, prompting it to assess truthfulness.
+
+#### Training Hyperparameters
+
+- **Training regime:** `bfloat16` mixed precision
+- **Base model:** `google/gemma-2-9b-it`
+- **Epochs:** 5
+- **Learning rate:** 0.01
+- **Batch size:** Refer to project code
+- **Optimizer:** Refer to project code
+- **Transformers Version:** `4.44.2`
+
+## Evaluation
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+The model's evaluation methodology is described in "Truth Knows No Language: Evaluating Truthfulness Beyond English," using questions from the TruthfulQA-Multi dataset (English portion).
+
+#### Factors
+
+- **Language:** English.
+- **Model Type (of models being judged):** Base and instruction-tuned LLMs.
+- **Evaluation Metric:** Correlation of LLM-as-a-Judge scores with human judgments on truthfulness; comparison with multiple-choice metrics (MC2).
+
+#### Metrics
+
+- **Primary Metric:** Spearman correlation between the judge model's scores and human-annotated scores for truthfulness.
+- The paper found that LLM-as-a-Judge (like this model) correlates more closely with human judgments than multiple-choice metrics. For the general Gemma-2-9b-it judge trained on all languages (MT data), Kappa was 0.74 for English (Table 3 in paper).
+
+### Results
+
+#### Summary
+
+As reported in "Truth Knows No Language: Evaluating Truthfulness Beyond English":
+- LLMs generally perform best in English.
+- LLM-as-a-Judge models demonstrated a stronger correlation with human judgments compared to MC2 metrics.
+- This specific model (`gemma9b_instruct_truth_judge`) is one of the judge models fine-tuned for the experiments. Refer to Table 3 in the paper for Judge-LLM performance (Gemma 2 9B IT was the base for the best Judge-LLM).
+
+## Technical Specifications
+
+### Model Architecture and Objective
+
+The model is based on the `Gemma2` architecture (`Gemma2ForCausalLM`). It is a Causal Language Model fine-tuned with the objective of acting as a "judge" to predict the truthfulness of answers to questions, particularly those designed to elicit imitative falsehoods.
+- **Hidden Size:** 3584
+- **Intermediate Size:** 14336
+- **Num Attention Heads:** 16
+- **Num Hidden Layers:** 42
+- **Num Key Value Heads:** 8
+- **Vocab Size:** 256000
+
+### Compute Infrastructure
+
+- **Hardware:** Refer to project for details.
+- **Software:** PyTorch, Transformers `4.44.2`
+
+## Citation
+
+**Paper:**
+'''bibtex
+@inproceedings{calvo-etal-2025-truthknowsnolanguage,
+    title = "Truth Knows No Language: Evaluating Truthfulness Beyond English",
+    author = "Calvo Figueras, Blanca and Sagarzazu, Eneko and Etxaniz, Julen and Barnes, Jeremy and Gamallo, Pablo and De Dios Flores, Iria and Agerri, Rodrigo",
+    year={2025},
+    eprint={2502.09387},
+    archivePrefix={arXiv},
+    primaryClass={cs.CL},
+    url={https://arxiv.org/abs/2502.09387}
+}
+'''
+
+## More Information
+
+For more details on the methodology, dataset, and findings, please refer to the full paper "Truth Knows No Language: Evaluating Truthfulness Beyond English" and the project repository: `https://github.com/hitz-zentroa/truthfulqa-multi`.
+
+## Model Card Authors
+
+This model card was generated based on information from the paper "Truth Knows No Language: Evaluating Truthfulness Beyond English" by Blanca Calvo Figueras et al., and adapted from the Hugging Face model card template. Content populated by GitHub Copilot.
+
+## Model Card Contact
+
+For questions about the model or the research, please contact:
+- Blanca Calvo Figueras: `[email protected]`
+- Rodrigo Agerri: `[email protected]`