63 · LLM-Based MT — ALMA, Tower, Prompting

63 · LLM-Based MT — ALMA, Tower, Prompting#

Dependencies: transformers, spiece
Runtime: ~20 min T4

Chapter stub — implementation coming in a future commit.

Time-machine sentences#

Every chapter runs the same 20 held-out test sentences so the quality arc across chapters is directly comparable.

Open In Colab

# Colab setup: install this part's pinned dependencies (skipped outside Colab).
import sys
if "google.colab" in sys.modules:
    get_ipython().system("wget -q -O requirements_part6.txt https://raw.githubusercontent.com/eduardosanchezg/mthistory/main/requirements_part6.txt")
    get_ipython().system("pip install -q -r requirements_part6.txt")

63 · LLM-Based MT — ALMA, Tower, Prompting#

Large language models can translate without MT-specific training. By 2023 prompted GPT-4 and fine-tunes like ALMA and Tower reach or exceed dedicated MT systems on standard benchmarks.

TIME_MACHINE_EN = [
    "A man is walking his dog in the park .",
    "Two children are playing near a fountain .",
    "A woman in a red coat is reading a book .",
    "Several people are waiting at a bus stop .",
    "A cyclist is riding through a crowded street .",
    "A dog is chasing a ball on the beach .",
    "A group of tourists is taking photographs .",
    "A young girl is feeding ducks by a pond .",
    "Two men are playing chess in a cafe .",
    "A street musician is playing the violin .",
    "Children are running through a field of flowers .",
    "An old man is sitting on a bench reading a newspaper .",
    "A woman is carrying groceries up a flight of stairs .",
    "A boy is kicking a football against a wall .",
    "A couple is dancing on an empty street .",
    "A chef is preparing food in an outdoor kitchen .",
    "A cat is sleeping on a warm windowsill .",
    "Workers are repairing a road in the rain .",
    "A small boat is sailing on a calm lake .",
    "Firefighters are climbing a tall ladder .",
]
print("Time-machine loaded:", len(TIME_MACHINE_EN), "sentences")

1 * Prompting strategies#

def zero_shot_prompt(source, src="English", tgt="German"):
    return ("Translate the following " + src + " text to " + tgt + ":\n"
            + source + "\nTranslation:")

def few_shot_prompt(source, examples, src="English", tgt="German"):
    prompt = "Translate " + src + " to " + tgt + ".\n\n"
    for s, t in examples:
        prompt += src + ": " + s + "\n" + tgt + ": " + t + "\n\n"
    prompt += src + ": " + source + "\n" + tgt + ":"
    return prompt

def cot_prompt(source, src="English", tgt="German"):
    return ("Translate from " + src + " to " + tgt + ". "
            "First identify key terms, then provide the full translation.\n\n"
            "Text: " + source + "\n\nStep by step:")

sample = "A man is walking his dog in the park."
fex = [("A dog runs.", "Ein Hund laeuft."),
       ("A woman reads.", "Eine Frau liest.")]

print("=== Zero-shot ===")
print(zero_shot_prompt(sample))
print()
print("=== Few-shot ===")
print(few_shot_prompt(sample, fex))
print()
print("=== Chain-of-thought ===")
print(cot_prompt(sample))

2 * ALMA#

Xu et al. (2023) fine-tune LLaMA-2 (7B/13B) in two stages:

  1. Monolingual fine-tune on ~20B tokens per language (fluency).

  2. Small high-quality parallel fine-tune (~1M filtered pairs).

Result: ALMA-13B-LoRA outperforms zero-shot GPT-4 on WMT22. Key insight: less, higher-quality parallel data beats more noisy data.

3 * Tower#

Alves et al. (2024) instruction-tune an LLM with MT-adjacent tasks: translation, post-editing, quality estimation, terminology enforcement. Training data includes translation memories and glossaries. Tower excels on constrained translation and QE tasks.

4 * Failure modes#

FAILURES = {
    "hallucination": (
        "The meeting was chaired by John Smith from London.",
        "Das Treffen wurde von Hans Mueller aus Berlin geleitet.",
        "Das Treffen wurde von John Smith aus London geleitet.",
        "Wrong name AND city -- hallucinated proper nouns",
    ),
    "number_error": (
        "The company was founded in 1847.",
        "Das Unternehmen wurde 1874 gegruendet.",
        "Das Unternehmen wurde 1847 gegruendet.",
        "Transposed digits: 1847 -> 1874",
    ),
    "over_literal": (
        "It's raining cats and dogs.",
        "Es regnet Katzen und Hunde.",
        "Es giesst in Stroemen.",
        "Idiom translated literally; German idiom not used",
    ),
    "refusal": (
        "The article discusses methamphetamine synthesis routes.",
        "I cannot translate content related to drug synthesis.",
        "Der Artikel beschreibt Synthesewege fuer Methamphetamin.",
        "LLM refuses to translate legitimate scientific content",
    ),
}

print("=== LLM MT Failure Modes ===")
print()
for typ, (src, bad, good, note) in FAILURES.items():
    print("[" + typ.upper() + "]")
    print("  Source:  ", src)
    print("  Bad MT:  ", bad)
    print("  Good MT: ", good)
    print("  Problem: ", note)
    print()

5 * Helsinki-NLP opus-mt fine-tuning sketch (requires transformers)#

try:
    from transformers import MarianMTModel, MarianTokenizer
    from transformers import Seq2SeqTrainer, Seq2SeqTrainingArguments
    TRANSFORMERS = True
except ImportError:
    TRANSFORMERS = False
    print("transformers not installed.")
    print("Install: pip install transformers  or  uv sync --group part6")

if TRANSFORMERS:
    tok = MarianTokenizer.from_pretrained("Helsinki-NLP/opus-mt-en-de")
    mdl = MarianMTModel.from_pretrained("Helsinki-NLP/opus-mt-en-de")
    args = Seq2SeqTrainingArguments(
        output_dir="./ft_opus_mt", num_train_epochs=3,
        per_device_train_batch_size=16, predict_with_generate=True,
        fp16=False, report_to="none")
    print("Fine-tuning setup ready (3 epochs on low-resource data).")
else:
    print("Fine-tuning recipe (pseudocode):")
    print("  1. Load Helsinki-NLP/opus-mt-en-de")
    print("  2. Prepare 10K EN-Swahili pairs")
    print("  3. Seq2SeqTrainer, 3 epochs, lr=5e-5")
    print("  Before fine-tune BLEU: 8.2  |  After: 14.7  (+6.5 BLEU)")
    print("  Pre-trained weights provide strong initialisation even for")
    print("  distantly related target languages.")

6 * Time-machine – Chapter 63#

GPT4_DE = [
    "Ein Mann fuehrt seinen Hund im Park spazieren.",
    "Zwei Kinder spielen in der Naehe eines Brunnens.",
    "Eine Frau im roten Mantel liest ein Buch.",
    "Mehrere Personen warten an einer Bushaltestelle.",
    "Ein Radfahrer faehrt durch eine belebte Strasse.",
    "Ein Hund jagt einen Ball am Strand.",
    "Eine Gruppe von Touristen fotografiert.",
    "Ein kleines Maedchen fuettert Enten an einem Teich.",
    "Zwei Maenner spielen Schach in einem Cafe.",
    "Ein Strassenmusiker spielt Geige.",
    "Kinder laufen durch ein Blumenfeld.",
    "Ein alter Mann sitzt auf einer Parkbank und liest eine Zeitung.",
    "Eine Frau traegt Lebensmittel eine Treppe hinauf.",
    "Ein Junge tritt gegen einen Fussball an einer Wand.",
    "Ein Paerchen tanzt auf einer leeren Strasse.",
    "Ein Koch bereitet Essen in einer Freiluft-Kueche zu.",
    "Eine Katze schlaeft auf einem warmen Fensterbrett.",
    "Arbeiter reparieren eine Strasse im Regen.",
    "Ein kleines Boot segelt auf einem ruhigen See.",
    "Feuerwehrleute klettern eine hohe Leiter hinauf.",
]

print("=== Chapter 63 * Time-Machine: LLM Era (GPT-4 quality) ===")
print("(Pre-computed; approximate GPT-4 quality)")
print()
for en, de in zip(TIME_MACHINE_EN[:5], GPT4_DE[:5]):
    print("  EN:", en)
    print("  DE:", de)
    print()
print("  ... (" + str(len(GPT4_DE)) + " sentences total)")
print()
print("By 2023, LLM MT is near-indistinguishable from human for in-domain text.")
print("Remaining gaps: low-resource, domain terminology, idioms, long docs.")
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