LlamaLens

LlamaLens: Specialized Multilingual LLM forAnalyzing News and Social Media Content

Overview

LlamaLens is a specialized multilingual LLM designed for analyzing news and social media content. It focuses on 18 NLP tasks, leveraging 52 datasets across Arabic, English, and Hindi.

<p align="center"> <picture> <img width="352" alt="capablities_tasks_datasets" src="./llamalens-avatar.png"> </picture> </p>

Dataset

The model was trained on the LlamaLens dataset.

To Replicate the Experiments

The code to replicate the experiments is available on GitHub.

Model Inference

To utilize the LlamaLens model for inference, follow these steps:

1. Install the Required Libraries:

   Ensure you have the necessary libraries installed. You can do this using pip:

   pip install transformers torch
   

2. Load the Model and Tokenizer:: Use the transformers library to load the LlamaLens model and its tokenizer:

from transformers import AutoModelForCausalLM, AutoTokenizer

# Define model path
MODEL_PATH = "QCRI/LlamaLens"

# Load model and tokenizer
device_map = "auto"
model = AutoModelForCausalLM.from_pretrained(MODEL_PATH, device_map=device_map)
tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH, trust_remote_code=True)
tokenizer.pad_token = tokenizer.eos_token

1. Prepare the Input:: Tokenize your input text:

# Define task and input text
task = "classification"  # Change to "summarization" for summarization tasks
instruction = (
    "Analyze the text and indicate if it shows an emotion, then label it as joy, love, fear,"
    " anger, sadness, or surprise. Return only the label without any explanation, justification, or additional text."
)
input_text = "I am not creating anything I feel satisfied with."
output_prefix = "Summary: " if task == "summarization" else "Label: "

# Define messages for chat-based prompt format
messages = [
    {"role": "system", "content": "You are a social media expert providing accurate analysis and insights."},
    {"role": "user", "content": f"{instruction}\nInput: {input_text}"},
    {"role": "assistant", "content": output_prefix}
]

# Tokenize input
input_ids = tokenizer.apply_chat_template(
    messages,
    add_generation_prompt=False,
    continue_final_message=True,
    tokenize=True,
    padding=True,
    return_tensors="pt"
).to(model.device)

1. Generate the Output:: Generate a response using the model:

# Generate response
outputs = model.generate(
    input_ids,
    max_new_tokens=128,
    do_sample=False,
    eos_token_id=tokenizer.eos_token_id,
    pad_token_id=tokenizer.eos_token_id,
    temperature=0.001
)

# Decode and print response
response = tokenizer.decode(outputs[0][input_ids.shape[-1]:], skip_special_tokens=True)
print(response)

Results

Below, we present the performance of L-Lens: LlamaLens , where "Eng" refers to the English-instructed model and "Native" refers to the model trained with native language instructions. The results are compared against the SOTA (where available) and the Base: Llama-Instruct 3.1 baseline. The Δ (Delta) column indicates the difference between LlamaLens and the SOTA performance, calculated as (LlamaLens – SOTA).

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Arabic

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English

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Hindi

Paper

For an in-depth understanding, refer to our paper: LlamaLens: Specialized Multilingual LLM for Analyzing News and Social Media Content.

License

This model is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0).

Citation

Please cite our paper when using this model:

@inproceedings{kmainasi-etal-2025-llamalens,
    title = "{L}lama{L}ens: Specialized Multilingual {LLM} for Analyzing News and Social Media Content",
    author = "Kmainasi, Mohamed Bayan  and
      Shahroor, Ali Ezzat  and
      Hasanain, Maram  and
      Laskar, Sahinur Rahman  and
      Hassan, Naeemul  and
      Alam, Firoj",
    editor = "Chiruzzo, Luis  and
      Ritter, Alan  and
      Wang, Lu",
    booktitle = "Findings of the Association for Computational Linguistics: NAACL 2025",
    month = apr,
    year = "2025",
    address = "Albuquerque, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2025.findings-naacl.313/",
    doi = "10.18653/v1/2025.findings-naacl.313",
    pages = "5642--5664",
    ISBN = "979-8-89176-195-7",    
}